5.分类和词汇标注.ipynb

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 "cells": [
  {
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   "cell_type": "markdown",
   "id": "05ac8c4f",
   "metadata": {},
   "source": [
    "实验环境信息等内容网址：\n",
    "\n",
    "repo: [Streamer0320/NLP-3b | GitHub](https://github.com/Streamer0320/NLP-3b/)\n",
    "\n",
    "fork: [jin-hao-0320/NLP-3b | Gitee](https://gitee.com/jin-hao-0320/NLP-3b)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d708d39a",
   "metadata": {},
   "source": [
    "# 5. 分类和标注词汇\n",
    "\n",
    "早在小学你就学过名词、动词、形容词和副词之间的差异。这些“词类”不是闲置的文法家的发明，而是对许多语言处理任务都有用的分类。正如我们将看到的，这些分类源于对文本中词的分布的简单的分析。本章的目的是要回答下列问题：\n",
    "\n",
    "1. 什么是词汇分类，在自然语言处理中它们是如何使用？\n",
    "2. 一个好的存储词汇和它们的分类的Python数据结构是什么？\n",
    "3. 我们如何自动标注文本中词汇的词类？\n",
    "\n",
    "一路上，我们将介绍NLP的一些基本技术，包括序列标注、N-gram模型、回退和评估。这些技术在许多方面都很有用，标注为我们提供了一个表示它们的简单的上下文。我们还将看到，在典型的NLP处理流程中，标注为何是位于分词之后的第二个步骤。\n",
    "\n",
    "将单词按它们的词性分类并进行相应地标注的过程，称为词语性质标注、词性标注或简称标注。词性也称为词类或词汇类别。用于特定任务的标记的集合被称为一个标记集。我们在本章的重点是运用标记和自动标注文本。\n",
    "\n",
    "\n",
    "\n",
    "<a href=\"#1-使用词性标注器\">1 使用词性标注器</a>\n",
    "\n",
    "<a href=\"#2-已经标注的语料库\">2 已经标注的语料库</a>\n",
    "\n",
    "<a href=\"#3-使用Python字典映射单词到其属性\">3 使用Python字典映射单词到其属性</a>\n",
    "\n",
    "<a href=\"#41-默认标注器\">4.1 默认标注器</a>\n",
    "\n",
    "<a href=\"#5-N-gram标注\">5 N-gram标注</a>\n",
    "\n",
    "<a href=\"#6-基于转换的标注\">6 基于转换的标注</a>\n",
    "\n",
    "<a href=\"#7-如何确定一个词的分类\">7 如何确定一个词的分类</a>\n",
    "\n",
    "\n",
    "\n",
    "## 1 使用词性标注器\n",
    "\n",
    "一个词语性质标注器或者词性标注器处理一个单词序列，为每个词附加一个词性标记（不要忘记`import nltk`）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "18fdd177",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('And', 'CC'),\n",
       " ('now', 'RB'),\n",
       " ('for', 'IN'),\n",
       " ('something', 'NN'),\n",
       " ('completely', 'RB'),\n",
       " ('different', 'JJ')]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk import word_tokenize # 从 nltk 导入word_tokenize\n",
    "\n",
    "text = word_tokenize(\"And now for something completely different\")\n",
    "nltk.pos_tag(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d701a8e",
   "metadata": {},
   "source": [
    "在这里我们看到and是`CC`，并列连词；now和completely是`RB`，副词；for是`IN`，介词；something是`NN`，名词；different是`JJ`，形容词。\n",
    "\n",
    "注意\n",
    "\n",
    "NLTK为每个标记提供了文档，可以使用该标记来查询，如`nltk.help.upenn_tagset('RB')`，或者一个正则表达，如`nltk.help.upenn_tagset('NN.*')`。一些语料库有标记集文档的README文件，见`nltk.corpus.???.readme()`，用语料库的名称替换。\n",
    "\n",
    "让我们来看看另一个例子，这次包括一些同形同音异义词："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e3970736",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('They', 'PRP'),\n",
       " ('refuse', 'VBP'),\n",
       " ('to', 'TO'),\n",
       " ('permit', 'VB'),\n",
       " ('us', 'PRP'),\n",
       " ('to', 'TO'),\n",
       " ('obtain', 'VB'),\n",
       " ('the', 'DT'),\n",
       " ('refuse', 'NN'),\n",
       " ('permit', 'NN')]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = word_tokenize(\"They refuse to permit us to obtain the refuse permit\")\n",
    "nltk.pos_tag(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6392390",
   "metadata": {},
   "source": [
    "请注意refuse和permit都以现在时动词（`VBP`）和名词（`NN`）形式出现。例如refUSE是一个动词，意为“拒绝”，而REFuse是一个名词，意为“垃圾”（即它们不是同音词）。因此，我们需要知道正在使用哪一个词以便能正确读出文本。（出于这个原因，文本转语音系统通常进行词性标注。）\n",
    "\n",
    "注意\n",
    "\n",
    "**轮到你来：** 很多单词，如ski和race，可以用作名词或动词而发音没有区别。你能想出其他的吗？提示：想想一个常见的东西，尝试把词to放到它前面，看它是否也是一个动词；或者想想一个动作，尝试把the放在它前面，看它是否也是一个名词。现在用这个词的两种用途造句，在这句话上运行词性标注器。\n",
    "\n",
    "词汇类别如“名词”和词性标记如`NN`，看上去似乎有其用途，但在细节上将使许多读者感到晦涩。你可能想知道要引进这种额外的信息的理由是什么。很多这些类别源于对文本中单词分布的粗略分析。考虑下面的分析，涉及woman（名词），bought（动词），over（介词）和the（限定词）。`text.similar()`方法接收一个单词w，找出所有上下文w1w w2，然后找出所有出现在相同上下文中的词w'，即w1w'w2。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "62fdeae2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "man time day year car moment world house family child country boy\n",
      "state job place way war girl work word\n"
     ]
    }
   ],
   "source": [
    "text = nltk.Text(word.lower() for word in nltk.corpus.brown.words())\n",
    "text.similar('woman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8f9df68a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "made said done put had seen found given left heard was been brought\n",
      "set got that took in told felt\n"
     ]
    }
   ],
   "source": [
    "text.similar('bought')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6708c6b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in on to of and for with from at by that into as up out down through\n",
      "is all about\n"
     ]
    }
   ],
   "source": [
    "text.similar('over')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fe32d841",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a his this their its her an that our any all one these my in your no\n",
      "some other and\n"
     ]
    }
   ],
   "source": [
    "text.similar('the')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f47e3687",
   "metadata": {},
   "source": [
    "可以观察到，搜索woman找到名词；搜索bought找到的大部分是动词；搜索over一般会找到介词；搜索the找到几个限定词。一个标注器能够正确识别一个句子的上下文中的这些词的标记，例如The woman bought over $150,000 worth of clothes。\n",
    "\n",
    "一个标注器还可以为我们对未知词的认识建模，例如我们可以根据词根scrobble猜测scrobbling可能是一个动词，并有可能发生在he was scrobbling这样的上下文中。\n",
    "\n",
    "## 2 已经标注的语料库\n",
    "\n",
    "<a href=\"#21-表示已经标注的词符\">2.1 表示已经标注的词符</a>\n",
    "\n",
    "<a href=\"#22-读取已标注的语料库\">2.2 读取已标注的语料库</a>\n",
    "\n",
    "<a href=\"#23-通用词性标记集\">2.3 通用词性标记集</a>\n",
    "\n",
    "<a href=\"#24-名词\">2.4 名词</a>\n",
    "\n",
    "<a href=\"#25-动词\">2.5 动词</a>\n",
    "\n",
    "<a href=\"#26-形容词和副词\">2.6 形容词和副词</a>\n",
    "\n",
    "<a href=\"#27-未简化的标记\">2.7 未简化的标记</a>\n",
    "\n",
    "<a href=\"#28-探索已标注的语料库\">2.8 探索已标注的语料库</a>\n",
    "\n",
    "## 2.1 表示已经标注的词符\n",
    "\n",
    "按照NLTK的约定，一个已标注的词符使用一个由词符和标记组成的元组来表示。我们可以使用函数`str2tuple()`从表示一个已标注的词符的标准字符串创建一个这样的特殊元组："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "af3c368b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('fly', 'NN')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tagged_token = nltk.tag.str2tuple('fly/NN') # 从表示一个已标注的词符的标准字符串创建一个特殊元组\n",
    "tagged_token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8c33bb02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'fly'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tagged_token[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "570d5562",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NN'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tagged_token[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "537435ab",
   "metadata": {},
   "source": [
    "我们可以直接从一个字符串构造一个已标注的词符的列表。第一步是对字符串分词以便能访问单独的`单词/标记`字符串，然后将每一个转换成一个元组（使用`str2tuple()`）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1084e253",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('The', 'AT'),\n",
       " ('grand', 'JJ'),\n",
       " ('jury', 'NN'),\n",
       " ('commented', 'VBD'),\n",
       " ('on', 'IN'),\n",
       " ('a', 'AT'),\n",
       " ('number', 'NN'),\n",
       " ('of', 'IN'),\n",
       " ('other', 'AP'),\n",
       " ('topics', 'NNS'),\n",
       " (',', ','),\n",
       " ('AMONG', 'IN'),\n",
       " ('them', 'PPO'),\n",
       " ('the', 'AT'),\n",
       " ('Atlanta', 'NP'),\n",
       " ('and', 'CC'),\n",
       " ('Fulton', 'NP-TL'),\n",
       " ('County', 'NN-TL'),\n",
       " ('purchasing', 'VBG'),\n",
       " ('departments', 'NNS'),\n",
       " ('which', 'WDT'),\n",
       " ('it', 'PPS'),\n",
       " ('said', 'VBD'),\n",
       " ('``', '``'),\n",
       " ('ARE', 'BER'),\n",
       " ('well', 'QL'),\n",
       " ('operated', 'VBN'),\n",
       " ('and', 'CC'),\n",
       " ('follow', 'VB'),\n",
       " ('generally', 'RB'),\n",
       " ('accepted', 'VBN'),\n",
       " ('practices', 'NNS'),\n",
       " ('which', 'WDT'),\n",
       " ('inure', 'VB'),\n",
       " ('to', 'IN'),\n",
       " ('the', 'AT'),\n",
       " ('best', 'JJT'),\n",
       " ('interest', 'NN'),\n",
       " ('of', 'IN'),\n",
       " ('both', 'ABX'),\n",
       " ('governments', 'NNS'),\n",
       " (\"''\", \"''\"),\n",
       " ('.', '.')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent = '''\n",
    "The/AT grand/JJ jury/NN commented/VBD on/IN a/AT number/NN of/IN other/AP topics/NNS ,/, AMONG/IN them/PPO the/AT Atlanta/NP and/CC Fulton/NP-tl County/NN-tl purchasing/VBG departments/NNS which/WDT it/PPS said/VBD ``/`` ARE/BER well/QL operated/VBN and/CC follow/VB generally/RB accepted/VBN practices/NNS which/WDT inure/VB to/IN the/AT best/JJT interest/NN of/IN both/ABX governments/NNS ''/'' ./.\n",
    "'''\n",
    "[nltk.tag.str2tuple(t) for t in sent.split()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "303fd825",
   "metadata": {},
   "source": [
    "## 2.2 读取已标注的语料库\n",
    "\n",
    "NLTK中包括的若干语料库已标注了词性。下面是一个你用文本编辑器打开一个布朗语料库的文件就能看到的例子：\n",
    "\n",
    "> The/at Fulton/np-tl County/nn-tl Grand/jj-tl Jury/nn-tl said/vbd Friday/nr an/at investigation/nn of/in Atlanta's/np$ recent/jj primary/nn election/nn produced/vbd `/` no/at evidence/nn ''/'' that/cs any/dti irregularities/nns took/vbd place/nn ./.\n",
    "\n",
    "其他语料库使用各种格式存储词性标记。NLTK的语料库阅读器提供了一个统一的接口，使你不必理会这些不同的文件格式。与刚才提取并显示的上面的文件不同，布朗语料库的语料库阅读器按如下所示的方式表示数据。注意，词性标记已转换为大写的，自从布朗语料库发布以来这已成为标准的做法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "667c338d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('The', 'AT'), ('Fulton', 'NP-TL'), ...]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.corpus.brown.tagged_words() # 布朗词库 tagged_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "26e144d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('The', 'DET'), ('Fulton', 'NOUN'), ...]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.corpus.brown.tagged_words(tagset='universal')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02e87b07",
   "metadata": {},
   "source": [
    "只要语料库包含已标注的文本，NLTK的语料库接口都将有一个`tagged_words()`方法。下面是一些例子，再次使用布朗语料库所示的输出格式："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a80895aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('now', 'RB'), ('im', 'PRP'), ('left', 'VBD'), ...]\n"
     ]
    }
   ],
   "source": [
    "print(nltk.corpus.nps_chat.tagged_words())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ba497ab1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Confidence', 'NN'), ('in', 'IN'), ('the', 'DT'), ...]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.corpus.conll2000.tagged_words()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "de7f9bb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Pierre', 'NNP'), ('Vinken', 'NNP'), (',', ','), ...]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.corpus.treebank.tagged_words()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18747e22",
   "metadata": {},
   "source": [
    "并非所有的语料库都采用同一组标记；看前面提到的标记集的帮助函数和`readme()`方法中的文档。最初，我们想避免这些标记集的复杂化，所以我们使用一个内置的到“通用标记集“的映射："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0fca36fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('The', 'DET'), ('Fulton', 'NOUN'), ...]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.corpus.brown.tagged_words(tagset='universal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2ff20958",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Pierre', 'NOUN'), ('Vinken', 'NOUN'), (',', '.'), ...]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.corpus.treebank.tagged_words(tagset='universal')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3092a84a",
   "metadata": {},
   "source": [
    "NLTK中还有其他几种语言的已标注语料库，包括中文，印地语，葡萄牙语，西班牙语，荷兰语和加泰罗尼亚语。这些通常含有非ASCII文本，当输出较大的结构如列表时，Python总是以十六进制显示这些。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7f637f81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('一', 'Neu'), ('友情', 'Nad'), ('嘉珍', 'Nba'), ...]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.corpus.sinica_treebank.tagged_words()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "080a3ae4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('মহিষের', 'NN'), ('সন্তান', 'NN'), (':', 'SYM'), ...]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.corpus.indian.tagged_words()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "37733c2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Jersei', 'N'), ('atinge', 'V'), ('média', 'N'), ...]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.corpus.mac_morpho.tagged_words()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cc0c120b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Sao', 'NC'), ('Paulo', 'VMI'), ('(', 'Fpa'), ...]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.corpus.conll2002.tagged_words()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5c185164",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('El', 'da0ms0'), ('Tribunal_Suprem', 'np0000o'), ...]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.corpus.cess_cat.tagged_words()"
   ]
  },
  {
   "attachments": {
    "1c54b3124863d24d17b2edec4f1d47e5.png": {
     "image/png": 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    }
   },
   "cell_type": "markdown",
   "id": "a1b69a22",
   "metadata": {},
   "source": [
    "如果你的环境设置正确，有适合的编辑器和字体，你应该能够以人可读的方式显示单个字符串。例如，[2.1](https://usyiyi.github.io/nlp-py-2e-zh/5.html#fig-tag-indian)显示的使用`nltk.corpus.indian`访问的数据。\n",
    "\n",
    "![1c54b3124863d24d17b2edec4f1d47e5.png](attachment:1c54b3124863d24d17b2edec4f1d47e5.png))\n",
    "\n",
    "图 2.1：四种印度语言的词性标注数据：孟加拉语、印地语、马拉地语和泰卢固语\n",
    "\n",
    "如果语料库也被分割成句子，将有一个`tagged_sents()`方法将已标注的词划分成句子，而不是将它们表示成一个大列表。在我们开始开发自动标注器时，这将是有益的，因为它们在句子列表上被训练和测试，而不是词。\n",
    "\n",
    "## 2.3 通用词性标记集\n",
    "\n",
    "已标注的语料库使用许多不同的标记集约定来标注词汇。为了帮助我们开始，我们将看一看一个简化的标记集（[2.1](https://usyiyi.github.io/nlp-py-2e-zh/5.html#tab-universal-tagset)中所示）。\n",
    "\n",
    "表 2.1：\n",
    "\n",
    "通用词性标记集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2b579c99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('NOUN', 30654),\n",
       " ('VERB', 14399),\n",
       " ('ADP', 12355),\n",
       " ('.', 11928),\n",
       " ('DET', 11389),\n",
       " ('ADJ', 6706),\n",
       " ('ADV', 3349),\n",
       " ('CONJ', 2717),\n",
       " ('PRON', 2535),\n",
       " ('PRT', 2264),\n",
       " ('NUM', 2166),\n",
       " ('X', 92)]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.corpus import brown\n",
    "brown_news_tagged = brown.tagged_words(categories='news', tagset='universal')\n",
    "tag_fd = nltk.FreqDist(tag for (word, tag) in brown_news_tagged)\n",
    "tag_fd.most_common()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da89d268",
   "metadata": {},
   "source": [
    "注意\n",
    "\n",
    "**轮到你来：**使用`tag_fd.plot(cumulative=True)`为上面显示的频率分布绘图。标注为上述列表中的前五个标记的词的百分比是多少？\n",
    "\n",
    "我们可以使用这些标记做强大的搜索，结合一个图形化的词性索引工具`nltk.app.concordance()`。用它来寻找任一单词和词性标记的组合，如`N N N N`, `hit/VD`, `hit/VN`或者`the ADJ man`。\n",
    "\n",
    "## 2.4 名词\n",
    "\n",
    "名词一般指的是人、地点、事情或概念，例如: woman, Scotland, book, intelligence。名词可能出现在限定词和形容词之后，可以是动词的主语或宾语，如[2.2](https://usyiyi.github.io/nlp-py-2e-zh/5.html#tab-syntax-nouns)所示。\n",
    "\n",
    "表 2.2：\n",
    "\n",
    "一些名词的句法模式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "96af5f8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NOUN',\n",
       " 'DET',\n",
       " 'ADJ',\n",
       " 'ADP',\n",
       " '.',\n",
       " 'VERB',\n",
       " 'CONJ',\n",
       " 'NUM',\n",
       " 'ADV',\n",
       " 'PRT',\n",
       " 'PRON',\n",
       " 'X']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_tag_pairs = nltk.bigrams(brown_news_tagged)\n",
    "noun_preceders = [a[1] for (a, b) in word_tag_pairs if b[1] == 'NOUN']\n",
    "fdist = nltk.FreqDist(noun_preceders)\n",
    "[tag for (tag, _) in fdist.most_common()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "417fb385",
   "metadata": {},
   "source": [
    "这证实了我们的断言，名词出现在限定词和形容词之后，包括数字形容词（数词，标注为`NUM`）。\n",
    "\n",
    "## 2.5 动词\n",
    "\n",
    "动词是用来描述事件和行动的词，例如[2.3](https://usyiyi.github.io/nlp-py-2e-zh/5.html#tab-syntax-verbs)中的fall, eat。在一个句子中，动词通常表示涉及一个或多个名词短语所指示物的关系。\n",
    "\n",
    "表 2.3：\n",
    "\n",
    "一些动词的句法模式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b94b6da8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['is',\n",
       " 'said',\n",
       " 'was',\n",
       " 'are',\n",
       " 'be',\n",
       " 'has',\n",
       " 'have',\n",
       " 'will',\n",
       " 'says',\n",
       " 'would',\n",
       " 'were',\n",
       " 'had',\n",
       " 'been',\n",
       " 'could',\n",
       " \"'s\",\n",
       " 'can',\n",
       " 'do',\n",
       " 'say',\n",
       " 'make',\n",
       " 'may',\n",
       " 'did',\n",
       " 'rose',\n",
       " 'made',\n",
       " 'does',\n",
       " 'expected',\n",
       " 'buy',\n",
       " 'take',\n",
       " 'get',\n",
       " 'might',\n",
       " 'sell',\n",
       " 'added',\n",
       " 'sold',\n",
       " 'help',\n",
       " 'including',\n",
       " 'should',\n",
       " 'reported',\n",
       " 'according',\n",
       " 'pay',\n",
       " 'compared',\n",
       " 'being',\n",
       " 'fell',\n",
       " 'began',\n",
       " 'based',\n",
       " 'used',\n",
       " 'closed',\n",
       " \"'re\",\n",
       " 'want',\n",
       " 'see',\n",
       " 'took',\n",
       " 'yield',\n",
       " 'offered',\n",
       " 'set',\n",
       " 'priced',\n",
       " 'approved',\n",
       " 'come',\n",
       " 'noted',\n",
       " 'cut',\n",
       " 'ended',\n",
       " 'found',\n",
       " 'increased',\n",
       " 'become',\n",
       " 'think',\n",
       " 'named',\n",
       " 'go',\n",
       " 'trying',\n",
       " 'proposed',\n",
       " 'received',\n",
       " 'growing',\n",
       " 'declined',\n",
       " 'held',\n",
       " 'give',\n",
       " 'came',\n",
       " 'use',\n",
       " 'put',\n",
       " 'making',\n",
       " 'continue',\n",
       " 'raise',\n",
       " 'estimated',\n",
       " 'called',\n",
       " 'paid',\n",
       " 'designed',\n",
       " 'going',\n",
       " 'expects',\n",
       " 'seeking',\n",
       " 'must',\n",
       " 'plans',\n",
       " 'wo',\n",
       " 'increasing',\n",
       " 'saying',\n",
       " 'got',\n",
       " 'owns',\n",
       " 'trading',\n",
       " 'acquired',\n",
       " 'gained',\n",
       " 'fined',\n",
       " 'reached',\n",
       " 'holding',\n",
       " 'announced',\n",
       " 'filed',\n",
       " 'became',\n",
       " 'seek',\n",
       " 'included',\n",
       " 'using',\n",
       " 'led',\n",
       " 'decided',\n",
       " 'keep',\n",
       " 'disclosed',\n",
       " 'agreed',\n",
       " 'find',\n",
       " 'failed',\n",
       " 'run',\n",
       " 'taken',\n",
       " 'know',\n",
       " 'coming',\n",
       " \"'ve\",\n",
       " 'completed',\n",
       " 'built',\n",
       " 'raised',\n",
       " 'traded',\n",
       " 'lost',\n",
       " 'continued',\n",
       " 'paying',\n",
       " 'include',\n",
       " 'believe',\n",
       " 'receive',\n",
       " 'posted',\n",
       " 'wants',\n",
       " 'scheduled',\n",
       " 'went',\n",
       " 'indicated',\n",
       " 'provide',\n",
       " 'helped',\n",
       " 'needed',\n",
       " 'goes',\n",
       " 'told',\n",
       " 'result',\n",
       " 'need',\n",
       " 'caused',\n",
       " 'continues',\n",
       " 'dropped',\n",
       " 'showed',\n",
       " 'ordered',\n",
       " 'changed',\n",
       " 'face',\n",
       " 'continuing',\n",
       " 'launched',\n",
       " 'gives',\n",
       " 'reduce',\n",
       " \"'m\",\n",
       " 'lead',\n",
       " 'planned',\n",
       " 'wanted',\n",
       " 'following',\n",
       " 'remains',\n",
       " 'followed',\n",
       " 'remain',\n",
       " 'meet',\n",
       " 'believes',\n",
       " 'trade',\n",
       " 'includes',\n",
       " 'improve',\n",
       " 'buying',\n",
       " 'issued',\n",
       " 'rise',\n",
       " 'leading',\n",
       " 'ca',\n",
       " 'involved',\n",
       " 'known',\n",
       " 'like',\n",
       " 'makes',\n",
       " 'having',\n",
       " 'rejected',\n",
       " 'rising',\n",
       " 'increase',\n",
       " 'turned',\n",
       " 'operating',\n",
       " 'read',\n",
       " 'feel',\n",
       " 'win',\n",
       " 'given',\n",
       " 'prevent',\n",
       " 'offset',\n",
       " 'barred',\n",
       " 'earned',\n",
       " 'consented',\n",
       " 'support',\n",
       " 'gave',\n",
       " 'totaled',\n",
       " 'acquire',\n",
       " 'required',\n",
       " 'begin',\n",
       " 'require',\n",
       " 'offer',\n",
       " 'preferred',\n",
       " 'produced',\n",
       " 'bought',\n",
       " 'offering',\n",
       " 'asked',\n",
       " 'carry',\n",
       " 'build',\n",
       " 'takes',\n",
       " 'seem',\n",
       " 'introduced',\n",
       " 'taking',\n",
       " 'won',\n",
       " 'aimed',\n",
       " 'hurt',\n",
       " 'adds',\n",
       " 'cited',\n",
       " 'sought',\n",
       " 'bring',\n",
       " 'remaining',\n",
       " 'considered',\n",
       " 'suspended',\n",
       " 'prove',\n",
       " 'looking',\n",
       " 'doing',\n",
       " 'involving',\n",
       " 'opened',\n",
       " 'working',\n",
       " 'close',\n",
       " 'own',\n",
       " 'report',\n",
       " 'left',\n",
       " 'boosted',\n",
       " 'giving',\n",
       " 'fixed',\n",
       " 'listed',\n",
       " 'runs',\n",
       " 'purchasing',\n",
       " 'consider',\n",
       " 'prepared',\n",
       " 'hit',\n",
       " 'getting',\n",
       " 'alleged',\n",
       " 'look',\n",
       " 'allowed',\n",
       " 'approve',\n",
       " 'wrote',\n",
       " 'reduced',\n",
       " 'fall',\n",
       " 'advanced',\n",
       " 'appears',\n",
       " 'mixed',\n",
       " 'described',\n",
       " 'expect',\n",
       " 'manufacturing',\n",
       " 'spent',\n",
       " 'According',\n",
       " 'hopes',\n",
       " 'underlying',\n",
       " 'produce',\n",
       " 'work',\n",
       " 'placed',\n",
       " 'allow',\n",
       " 'sells',\n",
       " 'operate',\n",
       " 'extend',\n",
       " 'surged',\n",
       " 'jumped',\n",
       " 'boost',\n",
       " 'slowing',\n",
       " 'provided',\n",
       " 'seems',\n",
       " 'ending',\n",
       " 'selling',\n",
       " 'opposed',\n",
       " 'created',\n",
       " 'move',\n",
       " 'seen',\n",
       " 'comment',\n",
       " 'valued',\n",
       " 'losing',\n",
       " 'mature',\n",
       " 'provides',\n",
       " 'let',\n",
       " 'stopped',\n",
       " 'worked',\n",
       " 'thought',\n",
       " 'watch',\n",
       " 'introduce',\n",
       " 'related',\n",
       " 'improved',\n",
       " 'raising',\n",
       " 'seemed',\n",
       " 'force',\n",
       " 'beginning',\n",
       " 'leave',\n",
       " 'believed',\n",
       " 'stand',\n",
       " 'developed',\n",
       " 'decline',\n",
       " 'expire',\n",
       " 'managing',\n",
       " 'hold',\n",
       " 'invested',\n",
       " 'suggests',\n",
       " 'represent',\n",
       " 'settle',\n",
       " 'change',\n",
       " 'contributed',\n",
       " 'indicates',\n",
       " 'asking',\n",
       " 'elaborate',\n",
       " 'shows',\n",
       " 'refused',\n",
       " 'quoted',\n",
       " 'comes',\n",
       " 'ease',\n",
       " 'passed',\n",
       " 'Says',\n",
       " 'avoid',\n",
       " 'threatened',\n",
       " 'cause',\n",
       " 'end',\n",
       " 'violate',\n",
       " 'operates',\n",
       " 'kept',\n",
       " \"'ll\",\n",
       " 'soared',\n",
       " 'eliminated',\n",
       " 'becomes',\n",
       " 'create',\n",
       " 'print',\n",
       " 'shall',\n",
       " 'show',\n",
       " 'replaced',\n",
       " 'owned',\n",
       " 'argue',\n",
       " 'beat',\n",
       " 'elected',\n",
       " 'complete',\n",
       " 'issue',\n",
       " 'act',\n",
       " 'return',\n",
       " 'registered',\n",
       " 'suffer',\n",
       " 'worried',\n",
       " 'succeed',\n",
       " 'block',\n",
       " 'contain',\n",
       " 'occur',\n",
       " 'pursue',\n",
       " 'combined',\n",
       " 'requires',\n",
       " 'met',\n",
       " 'brought',\n",
       " 'started',\n",
       " 'covered',\n",
       " 'turn',\n",
       " 'running',\n",
       " 'eliminate',\n",
       " 'file',\n",
       " 'plunged',\n",
       " 'fallen',\n",
       " 'ran',\n",
       " 'fired',\n",
       " 'becoming',\n",
       " 'contained',\n",
       " 'managed',\n",
       " 'Take',\n",
       " 'suggested',\n",
       " 'accused',\n",
       " 'appeared',\n",
       " 'slow',\n",
       " 'helping',\n",
       " 'discovered',\n",
       " 'add',\n",
       " 'referred',\n",
       " 'oppose',\n",
       " 'stop',\n",
       " 'claim',\n",
       " 'sent',\n",
       " 'formed',\n",
       " 'resulting',\n",
       " 'turning',\n",
       " 'limited',\n",
       " 'shipped',\n",
       " 'forced',\n",
       " 'send',\n",
       " 'attract',\n",
       " 'admitting',\n",
       " 'denying',\n",
       " 'disgorge',\n",
       " 'attributed',\n",
       " 'causing',\n",
       " 'studied',\n",
       " 'resigned',\n",
       " 'cutting',\n",
       " 'voted',\n",
       " 'settled',\n",
       " 'expand',\n",
       " 'stood',\n",
       " 'retired',\n",
       " 'moved',\n",
       " 'pending',\n",
       " 'providing',\n",
       " 'anticipated',\n",
       " 'decide',\n",
       " 'creating',\n",
       " 'prompted',\n",
       " 'developing',\n",
       " 'start',\n",
       " 'maintained',\n",
       " 'expanding',\n",
       " 'follows',\n",
       " 'ranged',\n",
       " 'focused',\n",
       " 'climbed',\n",
       " 'reflect',\n",
       " 'insist',\n",
       " 'owed',\n",
       " 'happen',\n",
       " 'adjusted',\n",
       " 'awarded',\n",
       " 'reporting',\n",
       " 'talk',\n",
       " 'offers',\n",
       " 'written',\n",
       " 'urged',\n",
       " 'carried',\n",
       " 'identified',\n",
       " 'confirmed',\n",
       " 'playing',\n",
       " 'thinking',\n",
       " 'calls',\n",
       " 'means',\n",
       " 'tried',\n",
       " 'lying',\n",
       " 'asks',\n",
       " 'building',\n",
       " 'suggest',\n",
       " 'falling',\n",
       " 'discuss',\n",
       " 'matched',\n",
       " 'concluded',\n",
       " 'keeping',\n",
       " \"'d\",\n",
       " 'returned',\n",
       " 'withdrawn',\n",
       " 'bid',\n",
       " 'saw',\n",
       " 'signed',\n",
       " 'financing',\n",
       " 'assuming',\n",
       " 'adopted',\n",
       " 'attempting',\n",
       " 'accepted',\n",
       " 'cover',\n",
       " 'facing',\n",
       " 'risk',\n",
       " 'expanded',\n",
       " 'leaving',\n",
       " 'raises',\n",
       " 'declared',\n",
       " 'exercise',\n",
       " 'finished',\n",
       " 'regarding',\n",
       " 'finance',\n",
       " 'charge',\n",
       " 'starting',\n",
       " 'realize',\n",
       " 'felt',\n",
       " 'remained',\n",
       " 'expressed',\n",
       " 'done',\n",
       " 'replace',\n",
       " 'veto',\n",
       " 'stay',\n",
       " 'delivered',\n",
       " 'join',\n",
       " 'publishing',\n",
       " 'enters',\n",
       " 'appear',\n",
       " 'talking',\n",
       " 'heard',\n",
       " 'dumped',\n",
       " 'imported',\n",
       " 'assume',\n",
       " 'declining',\n",
       " 'capture',\n",
       " 'grew',\n",
       " 'holds',\n",
       " 'lift',\n",
       " 'treat',\n",
       " 'receiving',\n",
       " 'joined',\n",
       " 'reflecting',\n",
       " 'released',\n",
       " 'maintaining',\n",
       " 'cost',\n",
       " 'lowered',\n",
       " 'costs',\n",
       " 'exceed',\n",
       " 'gaining',\n",
       " 'considering',\n",
       " 'determined',\n",
       " 'ruled',\n",
       " 'hope',\n",
       " 'located',\n",
       " 'tied',\n",
       " 'heads',\n",
       " 'applied',\n",
       " 'failing',\n",
       " 'showing',\n",
       " 'apply',\n",
       " 'protecting',\n",
       " 'tells',\n",
       " 'complained',\n",
       " 'ask',\n",
       " 'triggered',\n",
       " 'entered',\n",
       " 'total',\n",
       " 'producing',\n",
       " 'denied',\n",
       " 'reducing',\n",
       " 'reflects',\n",
       " 'account',\n",
       " 'predicting',\n",
       " 'intended',\n",
       " 'purchased',\n",
       " 'blamed',\n",
       " 'worry',\n",
       " 'broken',\n",
       " 'carries',\n",
       " 'learned',\n",
       " 'renewed',\n",
       " 'walk',\n",
       " 'viewed',\n",
       " 'die',\n",
       " 'speculated',\n",
       " 'needs',\n",
       " 'gets',\n",
       " 'wait',\n",
       " 'caught',\n",
       " 'claims',\n",
       " 'choose',\n",
       " 'fail',\n",
       " 'insists',\n",
       " 'knows',\n",
       " 'pass',\n",
       " 'grown',\n",
       " 'gotten',\n",
       " 'grows',\n",
       " 'responded',\n",
       " 'play',\n",
       " 'try',\n",
       " 'mean',\n",
       " 'encourage',\n",
       " 'chosen',\n",
       " 'admits',\n",
       " 'Do',\n",
       " 'dismissed',\n",
       " 'realized',\n",
       " 'serve',\n",
       " 'associated',\n",
       " 'slipped',\n",
       " 'argued',\n",
       " 'citing',\n",
       " 'seeks',\n",
       " 'preventing',\n",
       " 'sparked',\n",
       " 'review',\n",
       " 'opening',\n",
       " 'fueled',\n",
       " 'negotiate',\n",
       " 'drop',\n",
       " 'pleased',\n",
       " 'spend',\n",
       " 'care',\n",
       " 'represents',\n",
       " 'spread',\n",
       " 'explains',\n",
       " 'push',\n",
       " 'divided',\n",
       " 'proposing',\n",
       " 'gain',\n",
       " 'representing',\n",
       " 'financed',\n",
       " 'execute',\n",
       " 'existing',\n",
       " 'discussed',\n",
       " 'uses',\n",
       " 'live',\n",
       " 'ring',\n",
       " 'signal',\n",
       " 'focus',\n",
       " 'hired',\n",
       " 'limit',\n",
       " 'begins',\n",
       " 'reflected',\n",
       " 'hear',\n",
       " 'determine',\n",
       " 'profit',\n",
       " 'marketed',\n",
       " 'warned',\n",
       " 'qualified',\n",
       " 'died',\n",
       " 'surviving',\n",
       " 'study',\n",
       " 'explained',\n",
       " 'imposed',\n",
       " 'recognize',\n",
       " 'eased',\n",
       " 'indicate',\n",
       " 'permit',\n",
       " 'retain',\n",
       " 'yielding',\n",
       " 'boosts',\n",
       " 'obtain',\n",
       " 'employs',\n",
       " 'banned',\n",
       " 'pointed',\n",
       " 'leaves',\n",
       " 'withdraw',\n",
       " 'refund',\n",
       " 'collected',\n",
       " 'upheld',\n",
       " 'benefited',\n",
       " 'doubled',\n",
       " 'attached',\n",
       " 'jump',\n",
       " 'compete',\n",
       " 'incurred',\n",
       " 'removed',\n",
       " 'honor',\n",
       " 'varying',\n",
       " 'marketing',\n",
       " 'protect',\n",
       " 'reach',\n",
       " 'share',\n",
       " 'requested',\n",
       " 'grant',\n",
       " 'stepping',\n",
       " 'direct',\n",
       " 'curb',\n",
       " 'controlling',\n",
       " 'snapped',\n",
       " 'tend',\n",
       " 'cast',\n",
       " 'clear',\n",
       " 'permitted',\n",
       " 'remove',\n",
       " 'revive',\n",
       " 'draw',\n",
       " 'predicted',\n",
       " 'suspect',\n",
       " 'coupled',\n",
       " 'belong',\n",
       " 'call',\n",
       " 'describes',\n",
       " 'played',\n",
       " 'committed',\n",
       " 'advertising',\n",
       " 'tested',\n",
       " 'revived',\n",
       " 'earns',\n",
       " 'feeling',\n",
       " 'turns',\n",
       " 'executed',\n",
       " 'killed',\n",
       " 'presented',\n",
       " 'deserve',\n",
       " 'mention',\n",
       " 'seeing',\n",
       " 'discussing',\n",
       " 'convicted',\n",
       " 'telling',\n",
       " 'agree',\n",
       " 'denies',\n",
       " 'pursued',\n",
       " 'spurred',\n",
       " 'accommodate',\n",
       " 'surrendered',\n",
       " 'gone',\n",
       " 'teach',\n",
       " 'booming',\n",
       " 'serving',\n",
       " 'restore',\n",
       " 'restructured',\n",
       " 'taught',\n",
       " 'feared',\n",
       " 'meant',\n",
       " 'adding',\n",
       " 'publishes',\n",
       " 'improving',\n",
       " 'ignoring',\n",
       " 'represented',\n",
       " 'funded',\n",
       " 'involve',\n",
       " 'charged',\n",
       " 'fared',\n",
       " 'eliminates',\n",
       " 'tumbled',\n",
       " 'redeemed',\n",
       " 'resolve',\n",
       " 'stem',\n",
       " 'hire',\n",
       " 'obtained',\n",
       " 'develop',\n",
       " 'employed',\n",
       " 'prohibits',\n",
       " 'promote',\n",
       " 'impose',\n",
       " 'consist',\n",
       " 'investigating',\n",
       " 'accepting',\n",
       " 'plan',\n",
       " 'insisted',\n",
       " 'contacted',\n",
       " 'note',\n",
       " 'driving',\n",
       " 'contends',\n",
       " 'cites',\n",
       " 'point',\n",
       " 'sign',\n",
       " 'printed',\n",
       " 'advertise',\n",
       " 'breaks',\n",
       " 'damaged',\n",
       " 'check',\n",
       " 'thinks',\n",
       " 'looming',\n",
       " 'expecting',\n",
       " 'exercised',\n",
       " 'auctioned',\n",
       " 'disappointed',\n",
       " 'subordinated',\n",
       " 'respond',\n",
       " 'secured',\n",
       " 'integrated',\n",
       " 'performed',\n",
       " 'targeting',\n",
       " 'stepped',\n",
       " 'moving',\n",
       " 'split',\n",
       " 'Buy',\n",
       " 'stemming',\n",
       " 'executing',\n",
       " 'shut',\n",
       " 'writing',\n",
       " 'regulated',\n",
       " 'stands',\n",
       " 'attend',\n",
       " 'reopen',\n",
       " 'drawn',\n",
       " 'Put',\n",
       " 'notes',\n",
       " 'weaken',\n",
       " 'trailed',\n",
       " 'deliver',\n",
       " 'occurred',\n",
       " 'changing',\n",
       " 'accept',\n",
       " 'expelled',\n",
       " 'amounted',\n",
       " 'scrutinizing',\n",
       " 'suspend',\n",
       " 'compares',\n",
       " 'backed',\n",
       " 'vote',\n",
       " 'covers',\n",
       " 'knew',\n",
       " 'joining',\n",
       " 'record',\n",
       " 'regarded',\n",
       " 'copy',\n",
       " 'conclude',\n",
       " 'ought',\n",
       " 'dominated',\n",
       " 'pushed',\n",
       " 'closing',\n",
       " 'rumored',\n",
       " 'respected',\n",
       " 'specified',\n",
       " 'understand',\n",
       " 'supported',\n",
       " 'perform',\n",
       " 'declaring',\n",
       " 'struggling',\n",
       " 'abandoned',\n",
       " 'considers',\n",
       " 'warning',\n",
       " 'offsetting',\n",
       " 'break',\n",
       " 'follow',\n",
       " 'figure',\n",
       " 'increases',\n",
       " 'proving',\n",
       " 'edged',\n",
       " 'Buying',\n",
       " 'forecast',\n",
       " 'specify',\n",
       " 'extended',\n",
       " 'tendered',\n",
       " 'unveiled',\n",
       " 'treating',\n",
       " 'exposed',\n",
       " 'industrialized',\n",
       " 'regulate',\n",
       " 'contracted',\n",
       " 'blip',\n",
       " 'vary',\n",
       " 'slid',\n",
       " 'succeeds',\n",
       " 'lifted',\n",
       " 'acts',\n",
       " 'welcomed',\n",
       " 'squeezed',\n",
       " 'fed',\n",
       " 'casting',\n",
       " 'prolonged',\n",
       " 'recorded',\n",
       " 'announce',\n",
       " 'reward',\n",
       " 'bowed',\n",
       " 'justify',\n",
       " 'asserted',\n",
       " 'appealing',\n",
       " 'faces',\n",
       " 'rule',\n",
       " 'inched',\n",
       " 'manufacture',\n",
       " 'fund',\n",
       " 'anticipates',\n",
       " 'link',\n",
       " 'describe',\n",
       " 'roll',\n",
       " 'calculate',\n",
       " 'transferring',\n",
       " 'favored',\n",
       " 'claiming',\n",
       " 'hurting',\n",
       " 'investing',\n",
       " 'trained',\n",
       " 'instituted',\n",
       " 'introducing',\n",
       " 'vowed',\n",
       " 'deemed',\n",
       " 'pose',\n",
       " 'concerned',\n",
       " 'accelerated',\n",
       " 'feels',\n",
       " 'solved',\n",
       " 'forgiven',\n",
       " 'stored',\n",
       " 'assembled',\n",
       " 'totaling',\n",
       " 'linked',\n",
       " 'forces',\n",
       " 'attempts',\n",
       " 'advertised',\n",
       " 'marks',\n",
       " 'sweeping',\n",
       " 'invest',\n",
       " 'kicked',\n",
       " 'brings',\n",
       " 'scrambled',\n",
       " 'diversify',\n",
       " 'swing',\n",
       " 'skyrocketed',\n",
       " 'targeted',\n",
       " 'repaid',\n",
       " 'open',\n",
       " 'crippled',\n",
       " 'lent',\n",
       " 'belongs',\n",
       " 'stressed',\n",
       " 'leveling',\n",
       " 'manufactured',\n",
       " 'pick',\n",
       " 'cite',\n",
       " 'provoke',\n",
       " 'last',\n",
       " 'climbing',\n",
       " 'Excluding',\n",
       " 'adjusting',\n",
       " 'counts',\n",
       " 'handle',\n",
       " 'polled',\n",
       " 'drink',\n",
       " 'sets',\n",
       " 'sidestep',\n",
       " 'fare',\n",
       " 'letting',\n",
       " 'entering',\n",
       " 'ban',\n",
       " 'visiting',\n",
       " 'endorsed',\n",
       " 'balked',\n",
       " 'compensate',\n",
       " 'terminated',\n",
       " 'modify',\n",
       " 'operated',\n",
       " 'entitles',\n",
       " 'romanticized',\n",
       " 'spends',\n",
       " 'condemned',\n",
       " 'competing',\n",
       " 'returning',\n",
       " 'murdered',\n",
       " 'load',\n",
       " 'lives',\n",
       " 'recommend',\n",
       " 'fighting',\n",
       " 'fills',\n",
       " 'charges',\n",
       " 'exist',\n",
       " 'Stung',\n",
       " 'banning',\n",
       " 'admitted',\n",
       " 'talks',\n",
       " 'launch',\n",
       " 'attracted',\n",
       " 'featured',\n",
       " 'devote',\n",
       " 'featuring',\n",
       " 'suing',\n",
       " 'shrinks',\n",
       " 'tripled',\n",
       " 'pumping',\n",
       " 'contributing',\n",
       " 'spur',\n",
       " 'concentrated',\n",
       " 'export',\n",
       " 'pull',\n",
       " 'approach',\n",
       " 'step',\n",
       " 'regard',\n",
       " 'breach',\n",
       " 'pleaded',\n",
       " 'inspired',\n",
       " 'defended',\n",
       " 'treated',\n",
       " 'casts',\n",
       " 'violated',\n",
       " 'enforce',\n",
       " 'surfaced',\n",
       " 'concentrate',\n",
       " 'stressing',\n",
       " 'suffered',\n",
       " 'loved',\n",
       " 'advised',\n",
       " 'studying',\n",
       " 'pushing',\n",
       " 'earn',\n",
       " 'save',\n",
       " 'interviewed',\n",
       " 'explain',\n",
       " 'gauge',\n",
       " 'measured',\n",
       " 'deny',\n",
       " 'hampered',\n",
       " 'fill',\n",
       " 'nominated',\n",
       " 'assured',\n",
       " 'finding',\n",
       " 'conducting',\n",
       " 'tracks',\n",
       " 'merge',\n",
       " 'merged',\n",
       " 'banking',\n",
       " 'achieve',\n",
       " 'acquiring',\n",
       " 'post',\n",
       " 'wish',\n",
       " 'retained',\n",
       " 'spark',\n",
       " 'chaired',\n",
       " 'deal',\n",
       " 'submit',\n",
       " 'sending',\n",
       " 'test',\n",
       " 'relegated',\n",
       " 'alleging',\n",
       " 'mounted',\n",
       " 'harass',\n",
       " 'crossing',\n",
       " 'involves',\n",
       " 'assist',\n",
       " 'killing',\n",
       " 'served',\n",
       " 'understood',\n",
       " 'touch',\n",
       " 'Asked',\n",
       " 'design',\n",
       " 'retaining',\n",
       " 'belonging',\n",
       " 'compiled',\n",
       " 'Guaranteed',\n",
       " 'positioned',\n",
       " 'plunging',\n",
       " 'locked',\n",
       " 'starts',\n",
       " 'waiting',\n",
       " 'rolled',\n",
       " 'lock',\n",
       " 'drifted',\n",
       " 'measures',\n",
       " 'Estimated',\n",
       " ...]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wsj = nltk.corpus.treebank.tagged_words(tagset='universal')\n",
    "word_tag_fd = nltk.FreqDist(wsj)\n",
    "[wt[0] for (wt, _) in word_tag_fd.most_common() if wt[1] == 'VERB']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e427b86e",
   "metadata": {},
   "source": [
    "请注意，频率分布中计算的项目是词-标记对。由于词汇和标记是成对的，我们可以把词作作为条件，标记作为事件，使用条件-事件对的链表初始化一个条件频率分布。这让我们看到了一个给定的词的标记的频率顺序列表："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2011518a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('VERB', 28), ('NOUN', 20)]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfd1 = nltk.ConditionalFreqDist(wsj)\n",
    "cfd1['yield'].most_common()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "89bb31c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('VERB', 25), ('NOUN', 3)]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfd1['cut'].most_common()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "063fbd44",
   "metadata": {},
   "source": [
    "我们可以颠倒配对的顺序，这样标记作为条件，词汇作为事件。现在我们可以看到对于一个给定的标记可能的词。我们将用《华尔街日报 》的标记集而不是通用的标记集来这样做："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "363e374d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['been',\n",
       " 'expected',\n",
       " 'made',\n",
       " 'compared',\n",
       " 'based',\n",
       " 'used',\n",
       " 'priced',\n",
       " 'sold',\n",
       " 'named',\n",
       " 'designed',\n",
       " 'held',\n",
       " 'fined',\n",
       " 'taken',\n",
       " 'paid',\n",
       " 'traded',\n",
       " 'increased',\n",
       " 'said',\n",
       " 'filed',\n",
       " 'reached',\n",
       " 'called',\n",
       " 'scheduled',\n",
       " 'disclosed',\n",
       " 'reported',\n",
       " 'proposed',\n",
       " 'estimated',\n",
       " 'set',\n",
       " 'known',\n",
       " 'built',\n",
       " 'approved',\n",
       " 'given',\n",
       " 'acquired',\n",
       " 'found',\n",
       " 'offered',\n",
       " 'received',\n",
       " 'caused',\n",
       " 'considered',\n",
       " 'ordered',\n",
       " 'required',\n",
       " 'preferred',\n",
       " 'led',\n",
       " 'issued',\n",
       " 'fixed',\n",
       " 'listed',\n",
       " 'prepared',\n",
       " 'involved',\n",
       " 'aimed',\n",
       " 'needed',\n",
       " 'launched',\n",
       " 'produced',\n",
       " 'put',\n",
       " 'planned',\n",
       " 'seen',\n",
       " 'alleged',\n",
       " 'valued',\n",
       " 'barred',\n",
       " 'become',\n",
       " 'related',\n",
       " 'improved',\n",
       " 'changed',\n",
       " 'provided',\n",
       " 'come',\n",
       " 'got',\n",
       " 'allowed',\n",
       " 'mixed',\n",
       " 'suspended',\n",
       " 'owned',\n",
       " 'elected',\n",
       " 'worried',\n",
       " 'completed',\n",
       " 'combined',\n",
       " 'raised',\n",
       " 'left',\n",
       " 'placed',\n",
       " 'invested',\n",
       " 'fallen',\n",
       " 'failed',\n",
       " 'helped',\n",
       " 'run',\n",
       " 'opposed',\n",
       " 'quoted',\n",
       " 'continued',\n",
       " 'threatened',\n",
       " 'offset',\n",
       " 'shipped',\n",
       " 'eliminated',\n",
       " 'followed',\n",
       " 'sought',\n",
       " 'hurt',\n",
       " 'replaced',\n",
       " 'covered',\n",
       " 'boosted',\n",
       " 'registered',\n",
       " 'focused',\n",
       " 'closed',\n",
       " 'adjusted',\n",
       " 'written',\n",
       " 'had',\n",
       " 'managed',\n",
       " 'discovered',\n",
       " 'withdrawn',\n",
       " 'agreed',\n",
       " 'lost',\n",
       " 'formed',\n",
       " 'reduced',\n",
       " 'limited',\n",
       " 'done',\n",
       " 'delivered',\n",
       " 'studied',\n",
       " 'rejected',\n",
       " 'retired',\n",
       " 'located',\n",
       " 'believed',\n",
       " 'turned',\n",
       " 'awarded',\n",
       " 'spent',\n",
       " 'broken',\n",
       " 'opened',\n",
       " 'caught',\n",
       " 'refused',\n",
       " 'created',\n",
       " 'grown',\n",
       " 'gotten',\n",
       " 'chosen',\n",
       " 'indicated',\n",
       " 'associated',\n",
       " 'sent',\n",
       " 'pleased',\n",
       " 'forced',\n",
       " 'described',\n",
       " 'divided',\n",
       " 'financed',\n",
       " 'qualified',\n",
       " 'collected',\n",
       " 'determined',\n",
       " 'anticipated',\n",
       " 'incurred',\n",
       " 'brought',\n",
       " 'developed',\n",
       " 'denied',\n",
       " 'requested',\n",
       " 'announced',\n",
       " 'ranged',\n",
       " 'tied',\n",
       " 'jumped',\n",
       " 'cast',\n",
       " 'permitted',\n",
       " 'intended',\n",
       " 'coupled',\n",
       " 'played',\n",
       " 'carried',\n",
       " 'tested',\n",
       " 'revived',\n",
       " 'viewed',\n",
       " 'executed',\n",
       " 'convicted',\n",
       " 'blamed',\n",
       " 'accused',\n",
       " 'introduced',\n",
       " 'pursued',\n",
       " 'spurred',\n",
       " 'passed',\n",
       " 'gone',\n",
       " 'fired',\n",
       " 'posted',\n",
       " 'funded',\n",
       " 'charged',\n",
       " 'returned',\n",
       " 'signed',\n",
       " 'redeemed',\n",
       " 'ended',\n",
       " 'heard',\n",
       " 'employed',\n",
       " 'adopted',\n",
       " 'accepted',\n",
       " 'fueled',\n",
       " 'decided',\n",
       " 'contacted',\n",
       " 'kept',\n",
       " 'met',\n",
       " 'hit',\n",
       " 'soared',\n",
       " 'exercised',\n",
       " 'auctioned',\n",
       " 'disappointed',\n",
       " 'subordinated',\n",
       " 'integrated',\n",
       " 'performed',\n",
       " 'triggered',\n",
       " 'discussed',\n",
       " 'regulated',\n",
       " 'drawn',\n",
       " 'renewed',\n",
       " 'gained',\n",
       " 'owed',\n",
       " 'noted',\n",
       " 'stopped',\n",
       " 'regarded',\n",
       " 'included',\n",
       " 'rumored',\n",
       " 'respected',\n",
       " 'specified',\n",
       " 'abandoned',\n",
       " 'cut',\n",
       " 'marketed',\n",
       " 'tendered',\n",
       " 'exposed',\n",
       " 'industrialized',\n",
       " 'imported',\n",
       " 'thought',\n",
       " 'voted',\n",
       " 'banned',\n",
       " 'prolonged',\n",
       " 'moved',\n",
       " 'applied',\n",
       " 'trained',\n",
       " 'instituted',\n",
       " 'removed',\n",
       " 'concerned',\n",
       " 'forgiven',\n",
       " 'assembled',\n",
       " 'maintained',\n",
       " 'linked',\n",
       " 'advertised',\n",
       " 'skyrocketed',\n",
       " 'surged',\n",
       " 'targeted',\n",
       " 'repaid',\n",
       " 'crippled',\n",
       " 'settled',\n",
       " 'manufactured',\n",
       " 'confirmed',\n",
       " 'operated',\n",
       " 'romanticized',\n",
       " 'murdered',\n",
       " 'presented',\n",
       " 'worked',\n",
       " 'prompted',\n",
       " 'Stung',\n",
       " 'attracted',\n",
       " 'dumped',\n",
       " 'released',\n",
       " 'tried',\n",
       " 'dismissed',\n",
       " 'interviewed',\n",
       " 'referred',\n",
       " 'matched',\n",
       " 'measured',\n",
       " 'retained',\n",
       " 'chaired',\n",
       " 'obtained',\n",
       " 'relegated',\n",
       " 'compiled',\n",
       " 'Guaranteed',\n",
       " 'positioned',\n",
       " 'locked',\n",
       " 'Estimated',\n",
       " 'Founded',\n",
       " 'printed',\n",
       " 'handled',\n",
       " 'damaged',\n",
       " 'pressed',\n",
       " 'allocated',\n",
       " 'mired',\n",
       " 'installed',\n",
       " 'perceived',\n",
       " 'spread',\n",
       " 'moderated',\n",
       " 'frozen',\n",
       " 'surprised',\n",
       " 'cited',\n",
       " 'purchased',\n",
       " 'publicized',\n",
       " 'identified',\n",
       " 'expressed',\n",
       " 'attached',\n",
       " 'rung',\n",
       " 'started',\n",
       " 'absorbed',\n",
       " 'entered',\n",
       " 'founded',\n",
       " 'desired',\n",
       " 'risen',\n",
       " 'sustained',\n",
       " 'loaded',\n",
       " 'expelled',\n",
       " 'engaged',\n",
       " 'pegged',\n",
       " 'backed',\n",
       " 'enacted',\n",
       " 'faced',\n",
       " 'committed',\n",
       " 'confined',\n",
       " 'upset',\n",
       " 'connected',\n",
       " 'organized',\n",
       " 'diminished',\n",
       " 'added',\n",
       " 'answered',\n",
       " 'oriented',\n",
       " 'dominated',\n",
       " 'pushed',\n",
       " 'Posted',\n",
       " 'supported',\n",
       " 'contained',\n",
       " 'entrusted',\n",
       " 'secured',\n",
       " 'begun',\n",
       " 'labeled',\n",
       " 'beaten',\n",
       " 'cooled',\n",
       " 'discontinued',\n",
       " 'lowered',\n",
       " 'warned',\n",
       " 'selected',\n",
       " 'bolstered',\n",
       " 'attributed',\n",
       " 'restructured',\n",
       " 'bought',\n",
       " 'split',\n",
       " 'diluted',\n",
       " 'subdued',\n",
       " 'liquidated',\n",
       " 'capitalized',\n",
       " 'told',\n",
       " 'specialized',\n",
       " 'died',\n",
       " 'diagnosed',\n",
       " 'classified',\n",
       " 'outlawed',\n",
       " 'tracked',\n",
       " 'lifted',\n",
       " 'ensnarled',\n",
       " 'renovated',\n",
       " 'recorded',\n",
       " 'accumulated',\n",
       " 'guaranteed',\n",
       " 'cluttered',\n",
       " 'expedited',\n",
       " 'disputed',\n",
       " 'refunded',\n",
       " 'scrapped',\n",
       " 'existed',\n",
       " 'Regarded',\n",
       " 'vowed',\n",
       " 'deemed',\n",
       " 'complained',\n",
       " 'accelerated',\n",
       " 'solved',\n",
       " 'achieved',\n",
       " 'assisted',\n",
       " 'incorporated',\n",
       " 'capped',\n",
       " 'kicked',\n",
       " 'scrambled',\n",
       " 'outpaced',\n",
       " 'burned',\n",
       " 'clobbered',\n",
       " 'climbed',\n",
       " 'alarmed',\n",
       " 'fattened',\n",
       " 'lent',\n",
       " 'amended',\n",
       " 'watched',\n",
       " 'rooted',\n",
       " 'welcomed',\n",
       " 'rationed',\n",
       " 'assigned',\n",
       " 'designated',\n",
       " 'surveyed',\n",
       " 'strapped',\n",
       " 'twinned',\n",
       " 'painted',\n",
       " 'accrued',\n",
       " 'swapped',\n",
       " 'laid',\n",
       " 'obsessed',\n",
       " 'Filmed',\n",
       " 'populated',\n",
       " 'condemned',\n",
       " 'chopped',\n",
       " 'speculated',\n",
       " 'scattered',\n",
       " 'superimposed',\n",
       " 'nurtured',\n",
       " 'interrogated',\n",
       " 'rusted',\n",
       " 'responded',\n",
       " 'cleaned',\n",
       " 'asked',\n",
       " 'knitted',\n",
       " 'tripled',\n",
       " 'Confronted',\n",
       " 'prosecuted',\n",
       " 'treated',\n",
       " 'enhanced',\n",
       " 'inflated',\n",
       " 'surfaced',\n",
       " 'educated',\n",
       " 'faded',\n",
       " 'stabbed',\n",
       " 'learned',\n",
       " 'crowded',\n",
       " 'overused',\n",
       " 'expunged',\n",
       " 'wanted',\n",
       " 'replicated',\n",
       " 'embroiled',\n",
       " 'hampered',\n",
       " 'assured',\n",
       " 'judged',\n",
       " 'polarized',\n",
       " 'Rekindled',\n",
       " 'protected',\n",
       " 'argued',\n",
       " 'prohibited',\n",
       " 'initiated',\n",
       " 'mailed',\n",
       " 'UPHELD',\n",
       " 'sparked',\n",
       " 'killed',\n",
       " 'appointed',\n",
       " 'exhibited',\n",
       " 'empowered',\n",
       " 'served',\n",
       " 'Related',\n",
       " 'buoyed',\n",
       " 'convinced',\n",
       " 'mollified',\n",
       " 'rolled',\n",
       " 'flooded',\n",
       " 'alienated',\n",
       " 'chastised',\n",
       " 'portrayed',\n",
       " 'relied',\n",
       " 'recycled',\n",
       " 'Asked',\n",
       " 'scared',\n",
       " 'serviced',\n",
       " 'Developed',\n",
       " 'converted',\n",
       " 'equipped',\n",
       " 'classed',\n",
       " 'depressed',\n",
       " 'enclosed',\n",
       " 'dropped',\n",
       " 'rarefied',\n",
       " 'zoomed',\n",
       " 'commanded',\n",
       " 'exhausted',\n",
       " 'talked',\n",
       " 'excited',\n",
       " 'overpriced',\n",
       " 'stated',\n",
       " 'shown',\n",
       " 'declared',\n",
       " 'postponed',\n",
       " 'rescheduled',\n",
       " 'clamped',\n",
       " 'collapsed',\n",
       " 'interested',\n",
       " 'escalated',\n",
       " 'stoked',\n",
       " 'clarified',\n",
       " 'crossed',\n",
       " 'milked',\n",
       " 'assumed',\n",
       " 'competed',\n",
       " 'borrowed',\n",
       " 'squeezed',\n",
       " 'switched',\n",
       " 'cultivated',\n",
       " 'tailored',\n",
       " 'troubled',\n",
       " 'synchronized',\n",
       " 'muffled',\n",
       " 'mounted',\n",
       " 'filled',\n",
       " 'fed',\n",
       " 'remarked',\n",
       " 'observed',\n",
       " 'dressed',\n",
       " 'decorated',\n",
       " 'unsettled',\n",
       " 'Put',\n",
       " 'tanked',\n",
       " 'invented',\n",
       " 'infringed',\n",
       " 'hired',\n",
       " 'afflicted',\n",
       " 'deteriorated',\n",
       " 'codified',\n",
       " 'discarded',\n",
       " 'pointed',\n",
       " 'defined',\n",
       " 'represented',\n",
       " 'consented',\n",
       " 'Reached',\n",
       " 'rectified',\n",
       " 'blocked',\n",
       " 'forgotten',\n",
       " 'tightened',\n",
       " 'reaped',\n",
       " 'touted',\n",
       " 'negotiated',\n",
       " 'Given',\n",
       " 'impressed',\n",
       " 'engineered',\n",
       " 'diversified',\n",
       " 'expanded',\n",
       " 'provoked',\n",
       " 'stripped',\n",
       " 'reallocated',\n",
       " 'instructed',\n",
       " 'drafted',\n",
       " 'repaired',\n",
       " 'altered',\n",
       " 'corrected',\n",
       " 'promised',\n",
       " 'proved',\n",
       " 'understood',\n",
       " 'composed',\n",
       " 'robbed',\n",
       " 'deprived',\n",
       " 'impaired',\n",
       " 'advised',\n",
       " 'mentioned',\n",
       " 'contributed',\n",
       " 'OFFERED',\n",
       " 'Annualized',\n",
       " 'clashed',\n",
       " 'enjoyed',\n",
       " 'recruited',\n",
       " 'Provided',\n",
       " 'predicated',\n",
       " 'endorsed',\n",
       " 'construed',\n",
       " 'read',\n",
       " 'nominated',\n",
       " 'wasted',\n",
       " 'favored',\n",
       " 'disapproved',\n",
       " 'ratified',\n",
       " 'characterized',\n",
       " 'won',\n",
       " 'mortgaged',\n",
       " 'reclaimed',\n",
       " 'parched',\n",
       " 'cushioned',\n",
       " 'shut',\n",
       " 'attempted',\n",
       " 'torn',\n",
       " 'staid',\n",
       " 'flirted',\n",
       " 'stepped',\n",
       " 'headlined',\n",
       " 'beleaguered',\n",
       " 'plunged',\n",
       " 'missed',\n",
       " 'preapproved',\n",
       " 'upheld',\n",
       " 'accounted',\n",
       " 'vested',\n",
       " 'shaken',\n",
       " 'heated',\n",
       " 'exacerbated',\n",
       " 'wedded',\n",
       " 'headed',\n",
       " 'curbed',\n",
       " 'entrenched',\n",
       " 'stacked',\n",
       " 'frightened',\n",
       " 'automated',\n",
       " 'earned',\n",
       " 'spooked',\n",
       " 'transformed',\n",
       " 'orchestrated',\n",
       " 'gored',\n",
       " 'proven',\n",
       " 'minted',\n",
       " 'practiced',\n",
       " 'implemented',\n",
       " 'feared',\n",
       " 'Left',\n",
       " 'appropriated',\n",
       " 'noticed',\n",
       " 'finished',\n",
       " 'documented',\n",
       " 'colored',\n",
       " 'Funded',\n",
       " 'Concerned',\n",
       " 'echoed',\n",
       " 'halted',\n",
       " 'recouped',\n",
       " 'hunted',\n",
       " 'overcome',\n",
       " 'terminated',\n",
       " 'Rated',\n",
       " 'insured',\n",
       " 'rated',\n",
       " 'looked',\n",
       " 'justified',\n",
       " 'structured',\n",
       " 'generated',\n",
       " 'overstated',\n",
       " 'midsized',\n",
       " 'assessed',\n",
       " 'chilled',\n",
       " 'hidden',\n",
       " 'inserted',\n",
       " 'harmed',\n",
       " 'stressed',\n",
       " 'directed',\n",
       " 'stimulated',\n",
       " 'overdone',\n",
       " 'calculated',\n",
       " 'Named',\n",
       " 'revised',\n",
       " 'conducted',\n",
       " 'snapped',\n",
       " 'polled',\n",
       " 'topped',\n",
       " 'outdistanced',\n",
       " 'relaunched',\n",
       " 'repriced',\n",
       " 'concluded',\n",
       " 'hailed',\n",
       " 'despised',\n",
       " 'heightened',\n",
       " 'Used',\n",
       " 'nullified',\n",
       " 'puzzled',\n",
       " 'notified',\n",
       " 'merged',\n",
       " 'delisted',\n",
       " 'meant',\n",
       " 'own',\n",
       " 'singled',\n",
       " 'granted',\n",
       " 'felt',\n",
       " 'confused',\n",
       " 'complicated',\n",
       " 'muted',\n",
       " 'Continued',\n",
       " 'magnified',\n",
       " 'reimbursed',\n",
       " 'controlled',\n",
       " 'distributed',\n",
       " 'experienced',\n",
       " 'customized',\n",
       " 'extended',\n",
       " 'sweetened',\n",
       " 'derived',\n",
       " 'averted',\n",
       " 'slated',\n",
       " 'examined',\n",
       " 'reorganized',\n",
       " 'plagued',\n",
       " 'imposed',\n",
       " 'armed',\n",
       " 'rigged',\n",
       " 'subpoenaed',\n",
       " 'planted',\n",
       " 'centralized',\n",
       " 'resulted',\n",
       " 'forecast',\n",
       " 'fizzled',\n",
       " 'edged',\n",
       " 'battered',\n",
       " 'floated',\n",
       " 'spun',\n",
       " 'licensed',\n",
       " 'trimmed',\n",
       " 'detailed',\n",
       " 'born',\n",
       " 'fashioned',\n",
       " 'bribed',\n",
       " 'arrested',\n",
       " 'Absorbed',\n",
       " 'finalized',\n",
       " 'dashed',\n",
       " 'omitted',\n",
       " 'harvested',\n",
       " 'slowed',\n",
       " 'depleted',\n",
       " 'obligated',\n",
       " 'compressed',\n",
       " 'delayed',\n",
       " 'influenced',\n",
       " 'reflected',\n",
       " 'advanced',\n",
       " 'coated',\n",
       " 'shared']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wsj = nltk.corpus.treebank.tagged_words()\n",
    "cfd2 = nltk.ConditionalFreqDist((tag, word) for (word, tag) in wsj)\n",
    "list(cfd2['VBN'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9806d926",
   "metadata": {},
   "source": [
    "要弄清`VBD`（过去式）和`VBN`（过去分词）之间的区别，让我们找到可以同是`VBD`和`VBN`的词汇，看看一些它们周围的文字："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "113380e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[w for w in cfd1.conditions() if 'VBD' in cfd1[w] and 'VBN' in cfd1[w]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "537b018a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('While', 'IN'),\n",
       " ('program', 'NN'),\n",
       " ('trades', 'NNS'),\n",
       " ('swiftly', 'RB'),\n",
       " ('kicked', 'VBD')]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx1 = wsj.index(('kicked', 'VBD'))\n",
    "wsj[idx1-4:idx1+1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d73de694",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('head', 'NN'),\n",
       " ('of', 'IN'),\n",
       " ('state', 'NN'),\n",
       " ('has', 'VBZ'),\n",
       " ('kicked', 'VBN')]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx2 = wsj.index(('kicked', 'VBN'))\n",
    "wsj[idx2-4:idx2+1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0234889",
   "metadata": {},
   "source": [
    "在这种情况下，我们可以看到过去分词kicked前面是助动词have的形式。这是普遍真实的吗？\n",
    "\n",
    "注意\n",
    "\n",
    "**轮到你来：** 通过`list(cfd2['VN'])`指定一个过去分词的列表，尝试收集所有直接在列表中项目前面的词-标记对。\n",
    "\n",
    "## 2.6 形容词和副词\n",
    "\n",
    "另外两个重要的词类是形容词和副词。形容词修饰名词，可以作为修饰语（如the large pizza中的large），或者谓语（如the pizza is large）。英语形容词可以有内部结构（如the falling stocks中的fall+ing）。副词修饰动词，指定动词描述的事件的时间、方式、地点或方向（如the stocks fell quickly中的quickly）。副词也可以修饰的形容词（如Mary's teacher was really nice中的really）。\n",
    "\n",
    "英语中还有几个封闭的词类，如介词，冠词（也常称为限定词）（如the、a），情态动词（如should、may）和人称代词（如she、they）。每个词典和语法对这些词的分类都不同。\n",
    "\n",
    "注意\n",
    "\n",
    "**轮到你来：**如果你对这些词性中的一些不确定，使用`nltk.app.concordance()`学习它们，或看*Schoolhouse Rock!*语法视频于YouTube，或者查询本章结束的进一步阅读一节。\n",
    "\n",
    "## 2.7 未简化的标记\n",
    "\n",
    "让我们找出每个名词类型中最频繁的名词。[2.2](https://usyiyi.github.io/nlp-py-2e-zh/5.html#code-findtags)中的程序找出所有以`NN`开始的标记，并为每个标记提供了几个示例单词。你会看到有许多`NN`的变种；最重要有`$`表示所有格名词，`S`表示复数名词（因为复数名词通常以s结尾），以及`P`表示专有名词。此外，大多数的标记都有后缀修饰符：`-NC`表示引用，`-HL`表示标题中的词，`-TL`表示标题（布朗标记的特征）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f4a871d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NN [('year', 137), ('time', 97), ('state', 88), ('week', 85), ('man', 72)]\n",
      "NN$ [(\"year's\", 13), (\"world's\", 8), (\"state's\", 7), (\"nation's\", 6), (\"city's\", 6)]\n",
      "NN$-HL [(\"Golf's\", 1), (\"Navy's\", 1)]\n",
      "NN$-TL [(\"President's\", 11), (\"Administration's\", 3), (\"Army's\", 3), (\"League's\", 3), (\"University's\", 3)]\n",
      "NN-HL [('sp.', 2), ('problem', 2), ('Question', 2), ('cut', 2), ('party', 2)]\n",
      "NN-NC [('ova', 1), ('eva', 1), ('aya', 1)]\n",
      "NN-TL [('President', 88), ('House', 68), ('State', 59), ('University', 42), ('City', 41)]\n",
      "NN-TL-HL [('Fort', 2), ('Mayor', 1), ('Commissioner', 1), ('City', 1), ('Oak', 1)]\n",
      "NNS [('years', 101), ('members', 69), ('people', 52), ('sales', 51), ('men', 46)]\n",
      "NNS$ [(\"children's\", 7), (\"women's\", 5), (\"men's\", 3), (\"janitors'\", 3), (\"taxpayers'\", 2)]\n",
      "NNS$-HL [(\"Dealers'\", 1), (\"Idols'\", 1)]\n",
      "NNS$-TL [(\"Women's\", 4), (\"States'\", 3), (\"Giants'\", 2), (\"Princes'\", 1), (\"Bombers'\", 1)]\n",
      "NNS-HL [('Wards', 1), ('deputies', 1), ('bonds', 1), ('aspects', 1), ('Decisions', 1)]\n",
      "NNS-TL [('States', 38), ('Nations', 11), ('Masters', 10), ('Communists', 9), ('Rules', 9)]\n",
      "NNS-TL-HL [('Nations', 1)]\n"
     ]
    }
   ],
   "source": [
    "def findtags(tag_prefix, tagged_text):\n",
    "    cfd = nltk.ConditionalFreqDist((tag, word) for (word, tag) in tagged_text if tag.startswith(tag_prefix))\n",
    "    return dict((tag, cfd[tag].most_common(5)) for tag in cfd.conditions())\n",
    "\n",
    "tagdict = findtags('NN', nltk.corpus.brown.tagged_words(categories='news'))\n",
    "for tag in sorted(tagdict):\n",
    "   print(tag, tagdict[tag])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08b140f4",
   "metadata": {},
   "source": [
    "当我们开始在本章后续部分创建词性标注器时，我们将使用未简化的标记。\n",
    "\n",
    "## 2.8 探索已标注的语料库\n",
    "\n",
    "让我们简要地回过来探索语料库，我们在前面的章节中看到过，这次我们探索词性标记。\n",
    "\n",
    "假设我们正在研究词often，想看看它是如何在文本中使用的。我们可以试着看看跟在often后面的词汇"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3c35c5b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[',',\n",
       " '.',\n",
       " 'accomplished',\n",
       " 'analytically',\n",
       " 'appear',\n",
       " 'apt',\n",
       " 'associated',\n",
       " 'assuming',\n",
       " 'became',\n",
       " 'become',\n",
       " 'been',\n",
       " 'began',\n",
       " 'call',\n",
       " 'called',\n",
       " 'carefully',\n",
       " 'chose',\n",
       " 'classified',\n",
       " 'colorful',\n",
       " 'composed',\n",
       " 'contain',\n",
       " 'differed',\n",
       " 'difficult',\n",
       " 'encountered',\n",
       " 'enough',\n",
       " 'equate',\n",
       " 'extremely',\n",
       " 'found',\n",
       " 'happens',\n",
       " 'have',\n",
       " 'ignored',\n",
       " 'in',\n",
       " 'involved',\n",
       " 'more',\n",
       " 'needed',\n",
       " 'nightly',\n",
       " 'observed',\n",
       " 'of',\n",
       " 'on',\n",
       " 'out',\n",
       " 'quite',\n",
       " 'represent',\n",
       " 'responsible',\n",
       " 'revamped',\n",
       " 'seclude',\n",
       " 'set',\n",
       " 'shortened',\n",
       " 'sing',\n",
       " 'sounded',\n",
       " 'stated',\n",
       " 'still',\n",
       " 'sung',\n",
       " 'supported',\n",
       " 'than',\n",
       " 'to',\n",
       " 'when',\n",
       " 'work']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brown_learned_text = brown.words(categories='learned')\n",
    "sorted(set(b for (a, b) in nltk.bigrams(brown_learned_text) if a == 'often'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6812208f",
   "metadata": {},
   "source": [
    "然而，使用`tagged_words()`方法查看跟随词的词性标记可能更有指导性："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dcda9c3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VERB  ADV  ADP  ADJ    .  PRT \n",
      "  37    8    7    6    4    2 \n"
     ]
    }
   ],
   "source": [
    "brown_lrnd_tagged = brown.tagged_words(categories='learned', tagset='universal')\n",
    "tags = [b[1] for (a, b) in nltk.bigrams(brown_lrnd_tagged) if a[0] == 'often']\n",
    "fd = nltk.FreqDist(tags)\n",
    "fd.tabulate()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a51d274c",
   "metadata": {},
   "source": [
    "请注意often后面最高频率的词性是动词。名词从来没有在这个位置出现（在这个特别的语料中）。\n",
    "\n",
    "接下来，让我们看一些较大范围的上下文，找出涉及特定标记和词序列的词（在这种情况下，`\"<Verb> to <Verb>\"`）。在code-three-word-phrase中，我们考虑句子中的每个三词窗口 [# 1](https://usyiyi.github.io/nlp-py-2e-zh/5.html#three-word)，检查它们是否符合我们的标准 [# 2](https://usyiyi.github.io/nlp-py-2e-zh/5.html#verb-to-verb)。如果标记匹配，我们输出对应的词 [# 3](https://usyiyi.github.io/nlp-py-2e-zh/5.html#print-words)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f438a85b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "combined to achieve\n",
      "continue to place\n",
      "serve to protect\n",
      "wanted to wait\n",
      "allowed to place\n",
      "expected to become\n",
      "expected to approve\n",
      "expected to make\n",
      "intends to make\n",
      "seek to set\n",
      "like to see\n",
      "designed to provide\n",
      "get to hear\n",
      "expects to tell\n",
      "expected to give\n",
      "prefer to pay\n",
      "required to obtain\n",
      "permitted to teach\n",
      "designed to reduce\n",
      "Asked to elaborate\n",
      "got to go\n",
      "raised to pay\n",
      "scheduled to go\n",
      "cut to meet\n",
      "needed to meet\n",
      "hastened to add\n",
      "found to prevent\n",
      "continue to insist\n",
      "compelled to make\n",
      "made to remove\n",
      "revamped to give\n",
      "want to risk\n",
      "appear to spark\n",
      "fails to consider\n",
      "plans to call\n",
      "going to examine\n",
      "plans to name\n",
      "come to pass\n",
      "voted to accept\n",
      "happens to hold\n",
      "authorized to adopt\n",
      "hesitated to prosecute\n",
      "try to make\n",
      "decided to spend\n",
      "taken to preserve\n",
      "left to preserve\n",
      "stand to bring\n",
      "decided to seek\n",
      "trying to induce\n",
      "proposing to make\n",
      "decided to run\n",
      "directed to investigate\n",
      "expected to pass\n",
      "expected to make\n",
      "expected to encounter\n",
      "hopes to pass\n",
      "came to pay\n",
      "expected to receive\n",
      "understood to follow\n",
      "wanted to vote\n",
      "decide to call\n",
      "begin to flow\n",
      "appears to face\n",
      "fails to pass\n",
      "care to acknowledge\n",
      "like to convey\n",
      "continued to wage\n",
      "try to avoid\n",
      "done to arouse\n",
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      "added to encourage\n",
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      "needs to tell\n",
      "like to grow\n",
      "allowed to mature\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "used to attack\n",
      "continued to threaten\n",
      "decide to build\n",
      "used to destroy\n",
      "going to develop\n",
      "put to see\n",
      "fail to develop\n",
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      "fail to enter\n",
      "rehearing to acquire\n",
      "fail to convey\n",
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      "sponsored to win\n",
      "invited to judge\n",
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      "qualified to judge\n",
      "helping to create\n",
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      "built to accommodate\n",
      "bothered to make\n",
      "wanted to purchase\n",
      "managing to get\n",
      "formed to develop\n",
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      "meant to pertain\n",
      "designed to cater\n",
      "equipped to handle\n",
      "pulled to clear\n",
      "Tend to make\n",
      "progresses to insure\n",
      "Endeavor to get\n",
      "used to separate\n",
      "used to separate\n",
      "seems to know\n",
      "wanted to trot\n",
      "likes to trot\n",
      "wants to trot\n",
      "started to pace\n",
      "starting to go\n",
      "expected to race\n",
      "designed to push\n",
      "looks to run\n",
      "began to motor\n",
      "trained to drag\n",
      "fled to make\n",
      "seemed to know\n",
      "used to say\n",
      "preferred to get\n",
      "hope to cover\n",
      "want to miss\n",
      "scheduled to vanish\n",
      "vanish to make\n",
      "continued to live\n",
      "seem to cascade\n",
      "forget to buy\n",
      "fail to shorten\n",
      "intend to cook\n",
      "sized to fit\n",
      "continue to release\n",
      "wish to create\n",
      "trim to fit\n",
      "cut to fit\n",
      "help to prevent\n",
      "designed to take\n",
      "used to transport\n",
      "want to buy\n",
      "used to fasten\n",
      "help to keep\n",
      "needed to build\n",
      "designed to accommodate\n",
      "adjusted to suit\n",
      "used to cut\n",
      "want to avoid\n",
      "agreed to take\n",
      "planned to destroy\n",
      "allowed to issue\n",
      "managed to coerce\n",
      "want to know\n",
      "planning to bring\n",
      "urged to keep\n",
      "come to swim\n",
      "enjoined to look\n",
      "prepared to cope\n",
      "want to make\n",
      "allowed to dry\n",
      "pays to buy\n",
      "want to play\n",
      "expected to last\n",
      "plan to add\n",
      "want to start\n",
      "plan to buy\n",
      "going to need\n",
      "continue to reduce\n",
      "needed to arrive\n",
      "done to correct\n",
      "cost to make\n",
      "settled to find\n",
      "grew to fulfill\n",
      "attempted to restrict\n",
      "conceived to affirm\n",
      "prefer to believe\n",
      "tried to refashion\n",
      "begin to fear\n",
      "linger to haunt\n",
      "disciplined to serve\n",
      "trained to fulfill\n",
      "organized to furnish\n",
      "continued to paint\n",
      "made to weave\n",
      "try to place\n",
      "hesitate to add\n",
      "seemed to emphasize\n",
      "mean to project\n",
      "wishes to convey\n",
      "beginning to learn\n",
      "start to study\n",
      "said to start\n",
      "wished to get\n",
      "Try to push\n",
      "taken to see\n",
      "promises to open\n",
      "selected to operate\n",
      "used to measure\n",
      "employed to measure\n",
      "used to detect\n",
      "tend to reflect\n",
      "used to scan\n",
      "decided to enter\n",
      "preparing to matriculate\n",
      "continued to help\n",
      "intend to carry\n",
      "lived to see\n",
      "manage to experiment\n",
      "tempted to compare\n",
      "made to see\n",
      "caused to glow\n",
      "made to flow\n",
      "happened to place\n",
      "used to construct\n",
      "used to fill\n",
      "examine to make\n",
      "designed to counter\n",
      "tend to become\n",
      "try to share\n",
      "continue to make\n",
      "come to expect\n",
      "used to keep\n",
      "bound to increase\n",
      "trying to match\n",
      "going to take\n",
      "continue to exceed\n",
      "extended to compare\n",
      "continue to divorce\n",
      "fail to lower\n",
      "supposed to stand\n",
      "prepared to teach\n",
      "school to cover\n",
      "make to assess\n",
      "try to set\n",
      "encouraged to take\n",
      "try to maintain\n",
      "plan to limit\n",
      "make to assure\n",
      "afford to spend\n",
      "Aim to balance\n",
      "Check to see\n",
      "called to determine\n",
      "cooperate to launch\n",
      "designed to expose\n",
      "existing to acquaint\n",
      "like to organize\n",
      "begin to appreciate\n",
      "inclined to think\n",
      "needed to protect\n",
      "struggling to meet\n",
      "helping to account\n",
      "want to learn\n",
      "used to think\n",
      "fights to change\n",
      "tends to rise\n",
      "expects to extend\n",
      "learn to wear\n",
      "tried to make\n",
      "appears to lie\n",
      "attempted to provide\n",
      "found to allow\n",
      "planned to duplicate\n",
      "wants to interest\n",
      "want to risk\n",
      "want to use\n",
      "continue to learn\n",
      "meant to convey\n",
      "used to minimize\n",
      "try to wrestle\n",
      "trying to install\n",
      "plan to build\n",
      "equipped to handle\n",
      "equipped to package\n",
      "equipped to receive\n",
      "equipped to save\n",
      "plan to increase\n",
      "hopes to cut\n",
      "bound to swell\n",
      "begin to feel\n",
      "want to change\n",
      "arrange to rent\n",
      "plan to travel\n",
      "plan to visit\n",
      "like to start\n",
      "wish to budget\n",
      "wanting to rent\n",
      "want to see\n",
      "want to throw\n",
      "designed to work\n",
      "trying to prepare\n",
      "began to develop\n",
      "used to suggest\n",
      "wanted to polish\n",
      "pays to consider\n",
      "compelled to use\n",
      "began to fall\n",
      "begun to write\n",
      "made to seem\n",
      "attempting to direct\n",
      "pleased to call\n",
      "scheduled to nominate\n",
      "come to spend\n",
      "managed to irrigate\n",
      "stooped to scoop\n",
      "fall to show\n",
      "try to push\n",
      "begins to deteriorate\n",
      "used to like\n",
      "offered to ship\n",
      "hopes to find\n",
      "invented to hold\n",
      "learn to like\n",
      "labored to set\n",
      "set to receive\n",
      "entered to compete\n",
      "seem to make\n",
      "seemed to answer\n",
      "decided to use\n",
      "began to show\n",
      "Wishing to show\n",
      "learned to set\n",
      "forced to fly\n",
      "hope to break\n",
      "came to recognize\n",
      "turning to cup\n",
      "seems to creep\n",
      "going to live\n",
      "got to learn\n",
      "learn to live\n",
      "going to live\n",
      "like to sew\n",
      "love to run\n",
      "love to crack\n",
      "yearn to make\n",
      "tried to see\n",
      "love to dust\n",
      "like to become\n",
      "decide to write\n",
      "cause to exist\n",
      "learn to portray\n",
      "learn to portray\n",
      "began to advise\n",
      "taught to yield\n",
      "prefer to cope\n",
      "helps to explain\n",
      "surprised to bump\n",
      "seemed to brave\n",
      "begins to regard\n",
      "began to embezzle\n",
      "appears to endorse\n",
      "expected to like\n",
      "begin to assert\n",
      "began to challenge\n",
      "going to become\n",
      "helping to make\n",
      "began to stress\n",
      "began to describe\n",
      "fails to gain\n",
      "liked to play\n",
      "love to audition\n",
      "wanted to show\n",
      "like to see\n",
      "loved to dance\n",
      "try to bid\n",
      "wanted to go\n",
      "asked to leave\n",
      "continued to promote\n",
      "wished to meet\n",
      "hoping to see\n",
      "got to know\n",
      "paused to comfort\n",
      "hesitate to quote\n",
      "decided to see\n",
      "wanted to take\n",
      "need to know\n",
      "need to look\n",
      "beginning to protrude\n",
      "try to speak\n",
      "decides to proceed\n",
      "interested to hear\n",
      "seems to probe\n",
      "preferring to consider\n",
      "known to go\n",
      "amazed to realize\n",
      "seeking to help\n",
      "interpreted to conform\n",
      "explored to find\n",
      "trying to throw\n",
      "designed to find\n",
      "required to mark\n",
      "asked to consider\n",
      "seem to involve\n",
      "seems to smell\n",
      "seems to see\n",
      "learned to develop\n",
      "arranged to meet\n",
      "need to work\n",
      "want to raise\n",
      "need to bring\n",
      "expect to grow\n",
      "expected to work\n",
      "want to include\n",
      "going to produce\n",
      "want to hire\n",
      "want to buy\n",
      "want to hire\n",
      "going to farm\n",
      "need to know\n",
      "want to undertake\n",
      "want to buy\n",
      "wish to locate\n",
      "plan to sell\n",
      "want to go\n",
      "intend to raise\n",
      "cost to live\n",
      "expected to produce\n",
      "expect to get\n",
      "forced to lay\n",
      "trying to explain\n",
      "happened to light\n",
      "began to turn\n",
      "intended to warn\n",
      "used to transform\n",
      "forced to overcome\n",
      "begins to give\n",
      "failed to post\n",
      "refused to permit\n",
      "encouraged to beget\n",
      "obliged to obey\n",
      "united to push\n",
      "try to oppose\n",
      "made to impose\n",
      "wanted to clarify\n",
      "proposed to sail\n",
      "determined to catch\n",
      "forced to turn\n",
      "seemed to sense\n",
      "seemed to know\n",
      "tried to brush\n",
      "turning to repeat\n",
      "tried to persuade\n",
      "wanted to turn\n",
      "preparing to pacify\n",
      "forced to retreat\n",
      "contracted to supply\n",
      "forced to leave\n",
      "offering to bring\n",
      "attempt to bring\n",
      "decided to cast\n",
      "liked to tease\n",
      "going to buy\n",
      "com to sea\n",
      "drilled to follow\n",
      "born to command\n",
      "come to recognize\n",
      "allowed to account\n",
      "created to fan\n",
      "come to mean\n",
      "trying to make\n",
      "refusing to keep\n",
      "wishes to discuss\n",
      "want to ask\n",
      "want to tap\n",
      "said to use\n",
      "employed to see\n",
      "shoot to kill\n",
      "refused to touch\n",
      "threatened to shoot\n",
      "said to let\n",
      "begin to roll\n",
      "held to assure\n",
      "going to make\n",
      "managed to get\n",
      "wanted to play\n",
      "prepared to counterattack\n",
      "failed to rally\n",
      "tried to rape\n",
      "refused to speak\n",
      "called to look\n",
      "refused to say\n",
      "mean to suggest\n",
      "prepared to carry\n",
      "designed to overthrow\n",
      "trying to put\n",
      "needed to work\n",
      "disposed to exploit\n",
      "fail to see\n",
      "bound to fall\n",
      "tempted to place\n",
      "need to ask\n",
      "seek to undermine\n",
      "hand to show\n",
      "begun to spit\n",
      "threatened to ignite\n",
      "invited to take\n",
      "rushing to keep\n",
      "like to wander\n",
      "like to follow\n",
      "professing to believe\n",
      "want to show\n",
      "enabled to attend\n",
      "determined to exercise\n",
      "inclined to exaggerate\n",
      "like to go\n",
      "determined to marry\n",
      "said to sprinkle\n",
      "used to stop\n",
      "bought to apply\n",
      "supposed to cause\n",
      "said to give\n",
      "allowed to rest\n",
      "enters to spoil\n",
      "wish to deny\n",
      "allowed to warm\n",
      "wishes to entertain\n",
      "allowed to stand\n",
      "refused to accept\n",
      "wished to continue\n",
      "persuaded to accept\n",
      "decided to charge\n",
      "decided to go\n",
      "allowed to see\n",
      "agreed to take\n",
      "decide to send\n",
      "allowed to delay\n",
      "request to leave\n",
      "promised to treat\n",
      "declined to enter\n",
      "added to reinforce\n",
      "obliged to publish\n",
      "continue to serve\n",
      "plans to serve\n",
      "Hoping to cut\n",
      "obliged to announce\n",
      "wish to preserve\n",
      "heard to remark\n",
      "hopes to redress\n",
      "desires to walk\n",
      "prefer to take\n",
      "likes to measure\n",
      "proposed to corral\n",
      "intended to stay\n",
      "ceased to look\n",
      "manages to overlook\n",
      "troubled to read\n",
      "destined to go\n",
      "expected to go\n",
      "used to characterize\n",
      "helping to raise\n",
      "decided to stay\n",
      "used to refer\n",
      "needs to educate\n",
      "tended to tamp\n",
      "like to underline\n",
      "begun to falter\n",
      "intend to include\n",
      "wish to improve\n",
      "try to take\n",
      "prefers to designate\n",
      "want to join\n",
      "seeking to increase\n",
      "used to make\n",
      "used to make\n",
      "inclined to remain\n",
      "heard to say\n",
      "stopped to receive\n",
      "used to describe\n",
      "claimed to own\n",
      "tried to find\n",
      "meant to pay\n",
      "given to go\n",
      "wanted to make\n",
      "began to gallop\n",
      "attempting to lasso\n",
      "failing to encircle\n",
      "got to get\n",
      "trying to maneuver\n",
      "beginning to go\n",
      "began to pull\n",
      "began to draw\n",
      "like to go\n",
      "tend to compete\n",
      "doing to help\n",
      "begins to see\n",
      "Desiring to fill\n",
      "tends to look\n",
      "try to match\n",
      "played to win\n",
      "failed to make\n",
      "began to involve\n",
      "learning to bunt\n",
      "dared to dream\n",
      "dared to taunt\n",
      "refused to come\n",
      "undertook to set\n",
      "essayed to down\n",
      "began to prosper\n",
      "liked to wring\n",
      "happened to sit\n",
      "hastened to place\n",
      "began to offer\n",
      "seemed to indicate\n",
      "liked to imagine\n",
      "expected to know\n",
      "began to appear\n",
      "begin to store\n",
      "continue to sanction\n",
      "intended to demonstrate\n",
      "permitted to exercise\n",
      "encouraged to develop\n",
      "failing to behave\n",
      "appeared to regard\n",
      "seek to disprove\n",
      "tends to fuse\n",
      "prefers to adduce\n",
      "empowered to compel\n",
      "designed to prevent\n",
      "disposed to heed\n",
      "agree to let\n",
      "seeking to reform\n",
      "used to crowd\n",
      "Ask to see\n",
      "interested to learn\n",
      "pass to add\n",
      "demanding to know\n",
      "seems to threaten\n",
      "tried to describe\n",
      "tried to explain\n",
      "seem to feel\n",
      "got to play\n",
      "ceased to need\n",
      "like to live\n",
      "came to see\n",
      "threatens to break\n",
      "begins to fade\n",
      "begins to appear\n",
      "equipped to tell\n",
      "used to increase\n",
      "hired to repeat\n",
      "wished to make\n",
      "seems to exist\n",
      "means to choose\n",
      "struggle to insulate\n",
      "serves to crystallize\n",
      "doomed to become\n",
      "serves to illuminate\n",
      "failed to furnish\n",
      "encouraged to trade\n",
      "continued to come\n",
      "promised to send\n",
      "attempted to understand\n",
      "decided to send\n",
      "assigned to arrest\n",
      "planned to build\n",
      "offered to surrender\n",
      "gone to live\n",
      "returned to succeed\n",
      "expected to arrange\n",
      "expected to supply\n",
      "expected to entertain\n",
      "tried to bring\n",
      "provoked to use\n",
      "refused to let\n",
      "needed to outwit\n",
      "purporting to inform\n",
      "inspired to remind\n",
      "mean to tell\n",
      "trying to demonstrate\n",
      "went to visit\n",
      "continued to teach\n",
      "beginning to devise\n",
      "learn to like\n",
      "stood to watch\n",
      "planned to go\n",
      "trying to plan\n",
      "continues to grow\n",
      "learn to keep\n",
      "learned to polish\n",
      "left to meet\n",
      "fails to make\n",
      "resolved to maintain\n",
      "register to vote\n",
      "needs to make\n",
      "going to march\n",
      "proposed to defeat\n",
      "proposed to enter\n",
      "seems to require\n",
      "seem to violate\n",
      "tended to obscure\n",
      "continue to view\n",
      "try to coerce\n",
      "likes to believe\n",
      "continue to affect\n",
      "served to overcome\n",
      "tends to further\n",
      "help to reveal\n",
      "struggle to assert\n",
      "said to exist\n",
      "taken to guard\n",
      "done to increase\n",
      "ready to tick\n",
      "forced to hypothesize\n",
      "used to challenge\n",
      "permitted to authorize\n",
      "decides to break\n",
      "required to pass\n",
      "decide to clobber\n",
      "decided to reverse\n",
      "tried to start\n",
      "forbidden to fly\n",
      "required to copy\n",
      "began to move\n",
      "run to live\n",
      "started to glance\n",
      "try to memorize\n",
      "turned to look\n",
      "trying to talk\n",
      "started to decline\n",
      "continues to take\n",
      "continues to center\n",
      "come to see\n",
      "come to walk\n",
      "beginning to complain\n",
      "gather to sing\n",
      "began to converse\n",
      "began to relax\n",
      "hoped to become\n",
      "forced to restrict\n",
      "began to give\n",
      "asked to become\n",
      "trying to sell\n",
      "serves to stimulate\n",
      "seemed to lack\n",
      "offered to make\n",
      "assembled to warrant\n",
      "returned to preside\n",
      "sought to prevent\n",
      "expect to stand\n",
      "compelled to face\n",
      "continue to live\n",
      "refused to move\n",
      "refused to obey\n",
      "doomed to become\n",
      "tended to romanticize\n",
      "supposed to keep\n",
      "left to rest\n",
      "wants to see\n",
      "tended to dress\n",
      "designed to become\n",
      "begins to feel\n",
      "tends to depict\n",
      "transferred to become\n",
      "impelled to make\n",
      "seeks to make\n",
      "made to look\n",
      "happen to meet\n",
      "told to lie\n",
      "wanted to write\n",
      "tempted to say\n",
      "omitting to assert\n",
      "continue to live\n",
      "refused to permit\n",
      "served to maintain\n",
      "seem to make\n",
      "help to dispel\n",
      "trained to describe\n",
      "daring to abandon\n",
      "seem to resemble\n",
      "appears to lie\n",
      "tend to persist\n",
      "tried to describe\n",
      "like to call\n",
      "began to speak\n",
      "try to say\n",
      "attempting to make\n",
      "attempt to answer\n",
      "wanted to know\n",
      "designed to enhance\n",
      "intended to provide\n",
      "continue to suffer\n",
      "serves to make\n",
      "fails to reach\n",
      "beginning to crack\n",
      "drawn to join\n",
      "made to bite\n",
      "forbidden to swing\n",
      "seemed to hear\n",
      "hired to drive\n",
      "try to work\n",
      "forbidden to love\n",
      "made to represent\n",
      "starts to ride\n",
      "tries to stop\n",
      "begins to gather\n",
      "brought to pass\n",
      "tends to reflect\n",
      "tended to stratify\n",
      "chosen to use\n",
      "used to mean\n",
      "forced to respond\n",
      "tends to lose\n",
      "tends to express\n",
      "made to integrate\n",
      "try to get\n",
      "attempted to restrain\n",
      "wished to continue\n",
      "failed to flourish\n",
      "propose to go\n",
      "wished to segregate\n",
      "liked to fancy\n",
      "Deciding to become\n",
      "strove to see\n",
      "used to play\n",
      "returned to live\n",
      "proceeded to find\n",
      "likes to catch\n",
      "seems to care\n",
      "intends to save\n",
      "compelled to find\n",
      "wishes to continue\n",
      "ceasing to write\n",
      "stops to ask\n",
      "expected to fulfill\n",
      "tailored to meet\n",
      "want to say\n",
      "want to quote\n",
      "seems to realize\n",
      "primed to catch\n",
      "try to diagnose\n",
      "want to point\n",
      "used to regard\n",
      "seems to represent\n",
      "trying to draw\n",
      "wish to see\n",
      "used to include\n",
      "allowed to operate\n",
      "urged to produce\n",
      "afford to present\n",
      "decides to drop\n",
      "expect to abolish\n",
      "needed to pit\n",
      "tempted to blame\n",
      "hope to serve\n",
      "tried to remedy\n",
      "tends to express\n",
      "seem to believe\n",
      "permitted to return\n",
      "attempted to make\n",
      "prepared to demonstrate\n",
      "calculated to suggest\n",
      "seemed to disconcert\n",
      "known to make\n",
      "going to talk\n",
      "learns to focus\n",
      "chooses to subordinate\n",
      "wish to preserve\n",
      "cease to exist\n",
      "seem to constitute\n",
      "destined to fail\n",
      "wants to get\n",
      "began to understand\n",
      "wanted to capture\n",
      "liked to tell\n",
      "decided to migrate\n",
      "continued to trouble\n",
      "labored to finish\n",
      "decided to return\n",
      "waiting to go\n",
      "chosen to serve\n",
      "came to know\n",
      "helped to escape\n",
      "opened to admit\n",
      "happened to see\n",
      "brought to bear\n",
      "inclined to argue\n",
      "seeming to say\n",
      "prompted to write\n",
      "come to dominate\n",
      "used to illustrate\n",
      "prepared to find\n",
      "wish to argue\n",
      "begin to read\n",
      "plan to discuss\n",
      "come to call\n",
      "expect to find\n",
      "come to believe\n",
      "continue to pay\n",
      "tend to thump\n",
      "determined to prove\n",
      "learn to control\n",
      "used to frustrate\n",
      "trying to assert\n",
      "trying to expose\n",
      "comes to regard\n",
      "felt to indicate\n",
      "came to speak\n",
      "needed to explain\n",
      "required to make\n",
      "sought to make\n",
      "accustomed to think\n",
      "come to look\n",
      "undertook to give\n",
      "came to study\n",
      "bound to go\n",
      "tried to dazzle\n",
      "got to know\n",
      "presumed to address\n",
      "obliged to defend\n",
      "coming to spend\n",
      "learned to write\n",
      "obliged to send\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "began to trail\n",
      "began to fly\n",
      "tried to calm\n",
      "inspired to complete\n",
      "began to talk\n",
      "compelled to spend\n",
      "expected to carry\n",
      "tend to make\n",
      "liked to think\n",
      "trying to emulate\n",
      "wanted to know\n",
      "seemed to remember\n",
      "asked to yield\n",
      "made to look\n",
      "begun to lose\n",
      "failed to learn\n",
      "wanted to take\n",
      "writing to devote\n",
      "vowed to kneel\n",
      "hoping to lift\n",
      "forced to seek\n",
      "continue to urge\n",
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      "continue to maintain\n",
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      "attempting to present\n",
      "instructed to burn\n",
      "attempted to conclude\n",
      "equipped to handle\n",
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      "failed to amaze\n",
      "continued to search\n",
      "threatening to swallow\n",
      "pleased to see\n",
      "tried to discover\n",
      "disturbed to find\n",
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      "allowed to preach\n",
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      "hesitate to sacrifice\n",
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      "chosen to edit\n",
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      "tried to halt\n",
      "wanted to die\n",
      "returned to make\n",
      "like to believe\n",
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      "dash to get\n",
      "tried to talk\n",
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      "wish to deny\n",
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      "delighted to make\n",
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      "assigned to check\n",
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      "began to select\n",
      "began to specify\n",
      "failed to work\n",
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      "helped to deepen\n",
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      "preparing to test\n",
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      "persuaded to lend\n",
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      "attempted to get\n",
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      "begins to besiege\n",
      "beginning to attract\n",
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      "longing to see\n",
      "long to see\n",
      "trying to promote\n",
      "beginning to fill\n",
      "wants to linger\n",
      "chiseled to match\n",
      "trying to track\n",
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      "began to develop\n",
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      "began to play\n",
      "gathered to hear\n",
      "guaranteed to excite\n",
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      "sent to knoe\n",
      "preparing to deport\n",
      "told to leave\n",
      "constrained to move\n",
      "refused to let\n",
      "managed to get\n",
      "trying to get\n",
      "tending to arouse\n",
      "sent to get\n",
      "tried to arbitrate\n",
      "tried to arbitrate\n",
      "ordered to knock\n",
      "refused to attend\n",
      "determined to compel\n",
      "refused to sanction\n",
      "going to let\n",
      "attempt to prorate\n",
      "leaving to come\n",
      "please to write\n",
      "want to go\n",
      "want to teach\n",
      "wanted to go\n",
      "tried to picture\n",
      "waiting to hear\n",
      "came to live\n",
      "began to show\n",
      "tends to weaken\n",
      "seems to rise\n",
      "permitted to see\n",
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      "serve to sublimate\n",
      "wanted to close\n",
      "wished to keep\n",
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      "began to denounce\n",
      "afford to place\n",
      "try to fit\n",
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      "expected to prefer\n",
      "tend to bring\n",
      "tend to become\n",
      "tend to assimilate\n",
      "tend to converge\n",
      "disposed to question\n",
      "forced to realize\n",
      "assembled to bear\n",
      "beginning to tell\n",
      "hastened to dispatch\n",
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      "going to trouble\n",
      "wanted to borrow\n",
      "trying to pick\n",
      "helping to prevent\n",
      "preferred to sell\n",
      "think to take\n",
      "ordered to approach\n",
      "prepared to move\n",
      "come to pay\n",
      "refused to grant\n",
      "want to offend\n",
      "seeming to invalidate\n",
      "want to drive\n",
      "left to resist\n",
      "tempted to consider\n",
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      "allowed to appoint\n",
      "wanted to invest\n",
      "decided to stay\n",
      "live to see\n",
      "began to put\n",
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      "wanted to leave\n",
      "want to see\n",
      "come to say\n",
      "began to move\n",
      "went to visit\n",
      "got to drink\n",
      "seem to know\n",
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      "seem to fall\n",
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      "beginning to point\n",
      "trying to prove\n",
      "trying to sort\n",
      "Start to prepare\n",
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      "threatening to murder\n",
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      "training to keep\n",
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      "learned to dispute\n",
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      "inclined to inquire\n",
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      "cease to haunt\n",
      "wishes to express\n",
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      "struggle to keep\n",
      "begin to look\n",
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      "begins to dream\n",
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      "began to discover\n",
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      "organized to deal\n",
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      "love to suffer\n",
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      "castigates to liberate\n",
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      "refused to accept\n",
      "supposed to know\n",
      "working to become\n",
      "led to see\n",
      "asked to vote\n",
      "duplicated to form\n",
      "learn to play\n",
      "want to change\n",
      "learns to become\n",
      "began to emerge\n",
      "used to annoy\n",
      "stooping to dispense\n",
      "come to see\n",
      "preferred to keep\n",
      "used to give\n",
      "came to feel\n",
      "used to accomplish\n",
      "found to match\n",
      "required to store\n",
      "saved to represent\n",
      "saved to represent\n",
      "created to accommodate\n",
      "inspected to determine\n",
      "used to look\n",
      "serve to illustrate\n",
      "intended to decrease\n",
      "required to improve\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "italicized to guide\n",
      "seems to center\n",
      "operate to center\n",
      "serves to focus\n",
      "purport to represent\n",
      "hesitates to suggest\n",
      "mentioned to make\n",
      "trying to develop\n",
      "compelled to omit\n",
      "continue to show\n",
      "planning to use\n",
      "expecting to recover\n",
      "meant to move\n",
      "preferred to continue\n",
      "trying to find\n",
      "planned to exterminate\n",
      "trying to marry\n",
      "pledged to hold\n",
      "determined to create\n",
      "seemed to assure\n",
      "attempted to marry\n",
      "obliged to concede\n",
      "expected to democratize\n",
      "Failing to heed\n",
      "determined to keep\n",
      "tend to procrastinate\n",
      "even to repudiate\n",
      "served to minimize\n",
      "encouraged to state\n",
      "trying to unearth\n",
      "decided to remove\n",
      "decide to encourage\n",
      "prefer to hire\n",
      "go to work\n",
      "intended to provide\n",
      "continues to stress\n",
      "encouraged to adopt\n",
      "motivated to take\n",
      "expected to respond\n",
      "training to meet\n",
      "used to pay\n",
      "taken to indicate\n",
      "afford to trade\n",
      "afford to lose\n",
      "taken to exemplify\n",
      "Try to imagine\n",
      "trying to outfox\n",
      "trying to get\n",
      "invited to try\n",
      "try to imagine\n",
      "begin to affect\n",
      "tend to move\n",
      "required to pay\n",
      "seek to force\n",
      "act to accentuate\n",
      "begin to edge\n",
      "move to restrain\n",
      "continue to press\n",
      "trying to win\n",
      "bound to mean\n",
      "continue to move\n",
      "continue to rise\n",
      "operate to keep\n",
      "expected to put\n",
      "presumed to realize\n",
      "assembled to legislate\n",
      "sought to find\n",
      "tend to view\n",
      "wished to minimize\n",
      "designed to deal\n",
      "ordered to retain\n",
      "refused to permit\n",
      "refuse to exercise\n",
      "prepared to read\n",
      "wants to displace\n",
      "authorized to fashion\n",
      "choose to assert\n",
      "used to impose\n",
      "chooses to enforce\n",
      "heard to object\n",
      "goes to prove\n",
      "intended to obstruct\n",
      "entitled to sue\n",
      "entitled to sue\n",
      "appear to permit\n",
      "applied to eliminate\n",
      "entitled to sue\n",
      "entitled to sue\n",
      "elected to file\n",
      "elect to continue\n",
      "intended to ease\n",
      "permitted to survive\n",
      "furnished to probe\n",
      "want to make\n",
      "designed to minimize\n",
      "designed to elicit\n",
      "continued to arrive\n",
      "coded to permit\n",
      "wishing to sell\n",
      "endeavored to maintain\n",
      "directed to provide\n",
      "seeking to continue\n",
      "refused to assume\n",
      "concerned to leave\n",
      "attempt to settle\n",
      "continue to make\n",
      "seems to increase\n",
      "exists to show\n",
      "learning to control\n",
      "want to explore\n",
      "struggling to meet\n",
      "trying to learn\n",
      "continues to increase\n",
      "like to tease\n",
      "begins to decline\n",
      "begins to substitute\n",
      "learned to cooperate\n",
      "begins to participate\n",
      "helping to make\n",
      "failing to achieve\n",
      "trying to say\n",
      "learn to identify\n",
      "failing to make\n",
      "needs to know\n",
      "fails to meet\n",
      "expected to administer\n",
      "used to store\n",
      "permitted to see\n",
      "expected to study\n",
      "wish to note\n",
      "required to furnish\n",
      "want to provide\n",
      "attempt to represent\n",
      "hopes to encourage\n",
      "designed to help\n",
      "appointed to act\n",
      "expected to vote\n",
      "appointed to study\n",
      "tended to take\n",
      "attempted to act\n",
      "attempt to act\n",
      "attempt to act\n",
      "intend to act\n",
      "fail to take\n",
      "try to serve\n",
      "tended to use\n",
      "found to behave\n",
      "impelled to make\n",
      "attempt to analyze\n",
      "designed to reflect\n",
      "deemed to vary\n",
      "held to constitute\n",
      "seem to support\n",
      "designed to cover\n",
      "found to vary\n",
      "taken to rest\n",
      "needs to know\n",
      "attempts to stand\n",
      "wishing to know\n",
      "made to appear\n",
      "attempts to supply\n",
      "like to record\n",
      "claims to show\n",
      "mean to assert\n",
      "mean to assert\n",
      "prepared to say\n",
      "mean to say\n",
      "sought to express\n",
      "said to learn\n",
      "mean to say\n",
      "meant to say\n",
      "meant to say\n",
      "meant to express\n",
      "forced to go\n",
      "disposed to quarrel\n",
      "seem to present\n",
      "allowed to move\n",
      "tended to reflect\n",
      "beginning to appreciate\n",
      "begun to disturb\n",
      "venture to assign\n",
      "crystallized to find\n",
      "begin to show\n",
      "beginning to expand\n",
      "dared to give\n",
      "continued to employ\n",
      "began to creep\n",
      "seems to appear\n",
      "rode to arrest\n",
      "serve to quiet\n",
      "decided to ride\n",
      "prepared to fight\n",
      "allowed to see\n",
      "refused to give\n",
      "compelled to kill\n",
      "desiring to leave\n",
      "wanted to clean\n",
      "needed to hire\n",
      "continued to spring\n",
      "extended to include\n",
      "intending to use\n",
      "need to commit\n",
      "sought to place\n",
      "want to know\n",
      "want to know\n",
      "serve to contrast\n",
      "obligated to regard\n",
      "tried to make\n",
      "attempting to falsify\n",
      "continued to accuse\n",
      "continued to accuse\n",
      "dare to instigate\n",
      "acting to deliver\n",
      "seeking to free\n",
      "endeavoring to deliver\n",
      "doomed to suffer\n",
      "adopted to accomplish\n",
      "fails to honor\n",
      "began to mix\n",
      "began to paint\n",
      "trying to simulate\n",
      "begun to broaden\n",
      "seems to contain\n",
      "tends to assert\n",
      "seem to thrust\n",
      "seems to thrust\n",
      "shaded to pry\n",
      "tend to assert\n",
      "continued to remain\n",
      "tried to escape\n",
      "taxed to pay\n",
      "come to pass\n",
      "seem to believe\n",
      "continues to rise\n",
      "continue to charge\n",
      "beginning to discover\n",
      "continues to offer\n",
      "wants to see\n",
      "come to dominate\n",
      "put to compete\n",
      "refuse to acknowledge\n",
      "liked to work\n",
      "tend to vote\n",
      "going to cure\n",
      "tended to overlook\n",
      "made to replace\n",
      "decided to strip\n",
      "made to satisfy\n",
      "arranged to fit\n",
      "needed to take\n",
      "tried to restrict\n",
      "led to speculate\n",
      "intended to fill\n",
      "restored to go\n",
      "appear to push\n",
      "began to wash\n",
      "wish to address\n",
      "want to meet\n",
      "appeared to evoke\n",
      "tend to blunt\n",
      "try to build\n",
      "tend to become\n",
      "presume to lecture\n",
      "attempting to acquaint\n",
      "failed to state\n",
      "like to make\n",
      "presume to speak\n",
      "mean to live\n",
      "made to look\n",
      "designed to discover\n",
      "seems to use\n",
      "used to describe\n",
      "postulated to explain\n",
      "used to support\n",
      "seem to corroborate\n",
      "wants to hear\n",
      "comes to represent\n",
      "used to accompany\n",
      "seems to symbolize\n",
      "begins to appear\n",
      "begins to ramble\n",
      "help to set\n",
      "calculated to put\n",
      "decided to write\n",
      "seemed to open\n",
      "combine to create\n",
      "learned to use\n",
      "began to take\n",
      "wanted to tell\n",
      "wanted to substitute\n",
      "want to make\n",
      "come to determine\n",
      "begun to ebb\n",
      "intended to incorporate\n",
      "led to postulate\n",
      "hope to discover\n",
      "tended to emphasize\n",
      "fails to explore\n",
      "seeks to make\n",
      "helping to define\n",
      "trying to avoid\n",
      "trying to get\n",
      "made to symbolize\n",
      "kneels to kiss\n",
      "serve to travesty\n",
      "used to equate\n",
      "altered to show\n",
      "altered to show\n",
      "taken to branch\n",
      "attempt to execute\n",
      "used to name\n",
      "used to name\n",
      "used to generate\n",
      "used to select\n",
      "used to select\n",
      "used to specify\n",
      "used to specify\n",
      "expected to serve\n",
      "used to eliminate\n",
      "designed to handle\n",
      "made to take\n",
      "tended to float\n",
      "began to decrease\n",
      "began to build\n",
      "used to provide\n",
      "tend to ensure\n",
      "seems to strive\n",
      "stated to emphasize\n",
      "channeled to produce\n",
      "expected to replace\n",
      "developed to attack\n",
      "needed to translate\n",
      "needed to make\n",
      "used to deny\n",
      "required to localize\n",
      "found to protect\n",
      "used to demonstrate\n",
      "shown to undergo\n",
      "extended to include\n",
      "shown to undergo\n",
      "allowed to go\n",
      "used to classify\n",
      "found to keep\n",
      "made to group\n",
      "continued to supply\n",
      "serves to inactivate\n",
      "needed to inactivate\n",
      "thought to offer\n",
      "serves to extend\n",
      "served to extend\n",
      "required to accomplish\n",
      "found to compare\n",
      "required to remove\n",
      "required to cut\n",
      "tends to push\n",
      "required to cut\n",
      "seen to correlate\n",
      "beginning to advance\n",
      "managed to grow\n",
      "started to open\n",
      "seem to justify\n",
      "claimed to give\n",
      "used to denote\n",
      "required to cause\n",
      "combined to attain\n",
      "used to slit\n",
      "hope to compete\n",
      "helped to alleviate\n",
      "calculated to expand\n",
      "lowered to permit\n",
      "appears to produce\n",
      "intended to cover\n",
      "established to cover\n",
      "repeated to evaluate\n",
      "varied to obtain\n",
      "varied to conform\n",
      "used to provide\n",
      "allowed to reach\n",
      "required to counteract\n",
      "appear to hold\n",
      "utilized to alleviate\n",
      "investigated to allow\n",
      "enlarged to require\n",
      "employed to reduce\n",
      "wait to see\n",
      "adjusted to supply\n",
      "utilized to direct\n",
      "tried to make\n",
      "jumping to anticipate\n",
      "tried to tempt\n",
      "Try to get\n",
      "bothered to phone\n",
      "allowed to make\n",
      "go to school\n",
      "wants to pay\n",
      "seemed to speak\n",
      "wired to set\n",
      "tried to shake\n",
      "going to follow\n",
      "served to overheat\n",
      "seemed to crouch\n",
      "seemed to advance\n",
      "seemed to resist\n",
      "began to write\n",
      "refuse to mention\n",
      "needed to eat\n",
      "wanting to know\n",
      "stop to grasp\n",
      "dared to defy\n",
      "hoping to store\n",
      "going to get\n",
      "want to get\n",
      "trying to talk\n",
      "determined to go\n",
      "proposed to rebuild\n",
      "going to stay\n",
      "plan to repair\n",
      "Hope to see\n",
      "used to take\n",
      "want to kill\n",
      "intend to go\n",
      "wanted to hurt\n",
      "bother to think\n",
      "delighted to see\n",
      "began to weep\n",
      "began to move\n",
      "tried to push\n",
      "tried to rescue\n",
      "seemed to hold\n",
      "began to think\n",
      "strove to think\n",
      "run to tell\n",
      "fail to hear\n",
      "dared to wait\n",
      "dared to pat\n",
      "trying to push\n",
      "began to whirl\n",
      "started to worry\n",
      "tried to push\n",
      "wanted to get\n",
      "tryin to fuck\n",
      "tried to stifle\n",
      "seeking to kill\n",
      "failed to check\n",
      "tried to shut\n",
      "refuses to believe\n",
      "begun to study\n",
      "amazed to discover\n",
      "appear to reject\n",
      "trying to write\n",
      "want to weep\n",
      "love to know\n",
      "like to know\n",
      "seem to think\n",
      "striving to appear\n",
      "begin to get\n",
      "called to take\n",
      "bother to shave\n",
      "tried to stop\n",
      "going to stop\n",
      "like to hear\n",
      "itch to relieve\n",
      "prefer to eat\n",
      "wanted to wipe\n",
      "survive to talk\n",
      "fit to kill\n",
      "began to whip\n",
      "seems to hover\n",
      "waiting to know\n",
      "want to go\n",
      "decided to remain\n",
      "appeared to provide\n",
      "tried to outface\n",
      "turned to smirk\n",
      "used to tell\n",
      "began to speak\n",
      "tried to force\n",
      "knelt to tell\n",
      "tried to live\n",
      "seemed to grow\n",
      "continued to find\n",
      "going to allow\n",
      "continued to proclaim\n",
      "came to warn\n",
      "refused to believe\n",
      "refused to agree\n",
      "wanted to run\n",
      "going to comply\n",
      "started to run\n",
      "crouch to get\n",
      "seem to help\n",
      "close to emit\n",
      "twitching to dislodge\n",
      "descended to blot\n",
      "strained to hear\n",
      "going to let\n",
      "begun to walk\n",
      "wanted to take\n",
      "wanted to talk\n",
      "want to bring\n",
      "consented to postpone\n",
      "meant to keep\n",
      "want to call\n",
      "expecting to find\n",
      "designed to serve\n",
      "made to flow\n",
      "wanting to draw\n",
      "persuaded to come\n",
      "told to ask\n",
      "wish to make\n",
      "promised to speak\n",
      "moved to distort\n",
      "wanted to emphasize\n",
      "liking to work\n",
      "tries to unteach\n",
      "began to worry\n",
      "tried to insist\n",
      "moving to take\n",
      "tried to contain\n",
      "takes to drink\n",
      "aim to keep\n",
      "tried to find\n",
      "stopped to say\n",
      "trying to contain\n",
      "needed to say\n",
      "forgot to order\n",
      "began to dance\n",
      "planned to use\n",
      "began to shave\n",
      "began to pack\n",
      "seemed to care\n",
      "rose to speak\n",
      "continue to plague\n",
      "live to see\n",
      "poised to pour\n",
      "Remember to call\n",
      "trying to reach\n",
      "tried to reach\n",
      "used to love\n",
      "wants to go\n",
      "wanted to wring\n",
      "got to dancing\n",
      "trying to pick\n",
      "began to laugh\n",
      "trying to find\n",
      "going to get\n",
      "want to fight\n",
      "wanting to sock\n",
      "trying to take\n",
      "want to hear\n",
      "seemed to feel\n",
      "trying to help\n",
      "wait to get\n",
      "expect to escape\n",
      "try to fathom\n",
      "allowed to spend\n",
      "try to make\n",
      "wanted to know\n",
      "tried to get\n",
      "tempted to ask\n",
      "decided to tell\n",
      "surprised to find\n",
      "wished to show\n",
      "like to bring\n",
      "beg to inquire\n",
      "wanted to visit\n",
      "like to enact\n",
      "wished to create\n",
      "wish to deceive\n",
      "want to create\n",
      "going to take\n",
      "going to lose\n",
      "consented to meet\n",
      "rose to go\n",
      "chosen to read\n",
      "started to cross\n",
      "seemed to think\n",
      "started to undo\n",
      "longed to tell\n",
      "chose to read\n",
      "served to increase\n",
      "refused to bring\n",
      "got to stop\n",
      "want to take\n",
      "tried to order\n",
      "seeking to create\n",
      "hope to accomplish\n",
      "attempt to rise\n",
      "tried to rise\n",
      "began to crawl\n",
      "failed to reach\n",
      "began to creep\n",
      "began to crawl\n",
      "promised to take\n",
      "meant to shout\n",
      "longed to increase\n",
      "want to begin\n",
      "seemed to imply\n",
      "stopped to admire\n",
      "stayed to visit\n",
      "want to get\n",
      "tried to remember\n",
      "began to riffle\n",
      "promise to make\n",
      "like to travel\n",
      "tried to repair\n",
      "used to play\n",
      "beginning to thin\n",
      "fail to let\n",
      "lean to reach\n",
      "trying to get\n",
      "want to see\n",
      "beginning to see\n",
      "bother to wipe\n",
      "fit to shake\n",
      "going to wake\n",
      "care to get\n",
      "tried to stop\n",
      "expected to hear\n",
      "got to expect\n",
      "going to put\n",
      "got to thaw\n",
      "want to touch\n",
      "made to fall\n",
      "come to rest\n",
      "trying to attain\n",
      "stopped to gaze\n",
      "begun to turn\n",
      "beginning to cook\n",
      "began to develop\n",
      "begun to buy\n",
      "want to thin\n",
      "planned to graduate\n",
      "going to tell\n",
      "trying to get\n",
      "want to roast\n",
      "preferred to sit\n",
      "liked to sit\n",
      "seemed to come\n",
      "plan to conduct\n",
      "required to detect\n",
      "obliged to prepare\n",
      "began to rise\n",
      "attempting to hide\n",
      "begun to protest\n",
      "come to clean\n",
      "rose to put\n",
      "managed to persuade\n",
      "offered to prevail\n",
      "want to hear\n",
      "fumbling to untie\n",
      "want to leave\n",
      "going to work\n",
      "want to keep\n",
      "like to think\n",
      "began to wrap\n",
      "going to put\n",
      "agreed to correspond\n",
      "lived to see\n",
      "longing to fall\n",
      "ask to see\n",
      "seemed to stride\n",
      "agreed to think\n",
      "wished to spend\n",
      "dared to ask\n",
      "supposed to buy\n",
      "planning to greet\n",
      "began to wonder\n",
      "refused to let\n",
      "wanted to kill\n",
      "wanted to go\n",
      "seem to know\n",
      "going to become\n",
      "going to kill\n",
      "like to tell\n",
      "want to see\n",
      "get to come\n",
      "like to dance\n",
      "like to dance\n",
      "wanted to believe\n",
      "trying to put\n",
      "started to say\n",
      "Forgot to get\n",
      "started to say\n",
      "trying to make\n",
      "want to sit\n",
      "trying to get\n",
      "want to lease\n",
      "wanted to shadow\n",
      "trying to make\n",
      "tried to shadow\n",
      "going to join\n",
      "try to keep\n",
      "like to run\n",
      "wanted to case\n",
      "going to join\n",
      "wanted to hang\n",
      "go to pick\n",
      "began to back\n",
      "paused to feel\n",
      "decided to risk\n",
      "turned to face\n",
      "began to feel\n",
      "started to sweep\n",
      "startled to find\n",
      "meant to convey\n",
      "going to leave\n",
      "returning to seek\n",
      "bother to look\n",
      "try to run\n",
      "going to make\n",
      "began to wave\n",
      "gone to get\n",
      "Want to try\n",
      "going to call\n",
      "want to find\n",
      "want to see\n",
      "designed to put\n",
      "turned to see\n",
      "forced to use\n",
      "trying to drag\n",
      "start to angle\n",
      "tried to flatten\n",
      "managed to hunch\n",
      "brought to make\n",
      "surprised to find\n",
      "started to back\n",
      "want to try\n",
      "trying to catch\n",
      "used to keep\n",
      "forget to turn\n",
      "promised to observe\n",
      "started to plod\n",
      "tried to turn\n",
      "beginning to feel\n",
      "decided to indulge\n",
      "forgotten to turn\n",
      "meant to shut\n",
      "want to leave\n",
      "want to go\n",
      "got to remember\n",
      "got to put\n",
      "threaten to call\n",
      "need to take\n",
      "going to get\n",
      "get to know\n",
      "paused to look\n",
      "seemed to remember\n",
      "Happened to hear\n",
      "got to get\n",
      "begun to tell\n",
      "begun to question\n",
      "began to doubt\n",
      "wished to frighten\n",
      "need to break\n",
      "wished to make\n",
      "tried to give\n",
      "got to eat\n",
      "attempt to frighten\n",
      "trying to find\n",
      "want to talk\n",
      "refusing to bear\n",
      "Try to find\n",
      "want to go\n",
      "like to make\n",
      "going to go\n",
      "Remember to tell\n",
      "wanted to know\n",
      "tried to think\n",
      "tried to make\n",
      "began to nod\n",
      "began to sink\n",
      "tried to think\n",
      "tried to explain\n",
      "going to die\n",
      "going to die\n",
      "managed to cover\n",
      "going to believe\n",
      "expected to stand\n",
      "made to include\n",
      "bothering to whisper\n",
      "waiting to see\n",
      "agreed to fill\n",
      "waiting to report\n",
      "going to like\n",
      "going to louse\n",
      "installed to film\n",
      "going to see\n",
      "expect to get\n",
      "going to stand\n",
      "try to stop\n",
      "going to ask\n",
      "hoping to hear\n",
      "constrained to add\n",
      "wants to pass\n",
      "try to phone\n",
      "like to sing\n",
      "forced to undergo\n",
      "supposed to make\n",
      "wants to ask\n",
      "decided to let\n",
      "got to go\n",
      "tried to ignore\n",
      "going to keep\n",
      "startled to meet\n",
      "came to examine\n",
      "tried to bring\n",
      "reaching to release\n",
      "waiting to report\n",
      "used to paint\n",
      "Begin to look\n",
      "wanted to see\n",
      "Failing to find\n",
      "Try to forget\n",
      "seen to leave\n",
      "forced to give\n",
      "inclined to admit\n",
      "began to make\n",
      "professed to know\n",
      "asked to use\n",
      "leaving to keep\n",
      "fit to consult\n",
      "asked to see\n",
      "wanted to make\n",
      "continued to discharge\n",
      "seem to belong\n",
      "began to flicker\n",
      "trying to wreck\n",
      "fit to touch\n",
      "going to take\n",
      "trying to clear\n",
      "want to spend\n",
      "paused to look\n",
      "going to allow\n",
      "like to talk\n",
      "planning to set\n",
      "bent to examine\n",
      "turned to jump\n",
      "started to retch\n",
      "going to get\n",
      "come to recognize\n",
      "expected to report\n",
      "failed to see\n",
      "failed to notify\n",
      "failed to co-operate\n",
      "stopping to hear\n",
      "want to talk\n",
      "going to cost\n",
      "wanted to ask\n",
      "going to get\n",
      "going to swear\n",
      "tried to keep\n",
      "think to look\n",
      "tried to find\n",
      "bear to hold\n",
      "began to pace\n",
      "tried to tell\n",
      "intended to scare\n",
      "began to think\n",
      "hired to take\n",
      "going to send\n",
      "helped to create\n",
      "wanted to give\n",
      "led to believe\n",
      "trying to escape\n",
      "began to thrash\n",
      "get to work\n",
      "come to work\n",
      "want to see\n",
      "wanted to get\n",
      "want to go\n",
      "managed to swallow\n",
      "threatened to fire\n",
      "happen to see\n",
      "going to eat\n",
      "began to weep\n",
      "happens to ask\n",
      "want to rent\n",
      "Try to imagine\n",
      "promised to pay\n",
      "planning to remarry\n",
      "determined to get\n",
      "seemed to swell\n",
      "surprised to meet\n",
      "trying to tell\n",
      "known to run\n",
      "seem to preserve\n",
      "got to understand\n",
      "got to know\n",
      "came to ask\n",
      "asked to see\n",
      "began to abuse\n",
      "hurry to hang\n",
      "seemed to mind\n",
      "started to cross\n",
      "started to curse\n",
      "offering to buy\n",
      "began to feel\n",
      "seem to wink\n",
      "continued to stare\n",
      "beginning to feel\n",
      "like to listen\n",
      "want to study\n",
      "relieved to see\n",
      "seemed to notice\n",
      "decided to stay\n",
      "startled to see\n",
      "began to feel\n",
      "bothered to ask\n",
      "beginning to take\n",
      "began to wish\n",
      "want to encourage\n",
      "began to talk\n",
      "paused to moisten\n",
      "went to join\n",
      "got to go\n",
      "trying to get\n",
      "want to know\n",
      "want to take\n",
      "want to leave\n",
      "got to assume\n",
      "got to keep\n",
      "burning to light\n",
      "decided to leave\n",
      "set to stay\n",
      "going to kill\n",
      "go to bat\n",
      "stopped to cherish\n",
      "struggling to bridge\n",
      "learning to think\n",
      "delighted to encounter\n",
      "wanted to explain\n",
      "taught to grow\n",
      "Taught to grow\n",
      "opened to sell\n",
      "required to assume\n",
      "want to make\n",
      "got to hold\n",
      "want to inquire\n",
      "seemed to spend\n",
      "preoccupied to cook\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "asked to speak\n",
      "pacing to stare\n",
      "wished to make\n",
      "began to build\n",
      "needed to carry\n",
      "meaning to live\n",
      "agreed to help\n",
      "agreed to take\n",
      "contract to let\n",
      "refused to believe\n",
      "meant to invade\n",
      "going to turn\n",
      "got to intercept\n",
      "permitted to go\n",
      "failed to anticipate\n",
      "needed to make\n",
      "use to make\n",
      "help to repel\n",
      "seems to shield\n",
      "pretending to sleep\n",
      "threatening to report\n",
      "seem to concentrate\n",
      "want to give\n",
      "trying to pull\n",
      "adjusted to operate\n",
      "like to see\n",
      "bent to observe\n",
      "forced to accompany\n",
      "fear to tread\n",
      "programed to compute\n",
      "remember to program\n",
      "directed to develop\n",
      "schooled to examine\n",
      "appeared to require\n",
      "encouraged to develop\n",
      "remembered to introduce\n",
      "guided to make\n",
      "tried to run\n",
      "tried to tell\n",
      "tried to ask\n",
      "want to ask\n",
      "going to come\n",
      "going to happen\n",
      "going to happen\n",
      "going to take\n",
      "inclined to think\n",
      "manage to follow\n",
      "wanting to tell\n",
      "tried to write\n",
      "exhausted to stay\n",
      "afford to lose\n",
      "afford to pay\n",
      "used to work\n",
      "going to give\n",
      "began to rock\n",
      "tried to call\n",
      "stoop to catch\n",
      "began to paw\n",
      "began to nod\n",
      "hate to leave\n",
      "want to leave\n",
      "groped to reassemble\n",
      "begun to blow\n",
      "began to dissolve\n",
      "meant to help\n",
      "began to tire\n",
      "riding to kill\n",
      "got to get\n",
      "trying to decide\n",
      "try to cut\n",
      "began to weep\n",
      "going to walk\n",
      "tried to veer\n",
      "managed to bat\n",
      "Try to find\n",
      "began to explode\n",
      "racing to join\n",
      "began to snap\n",
      "began to buckle\n",
      "want to go\n",
      "seem to think\n",
      "goin' to move\n",
      "goin' to go\n",
      "goin' to help\n",
      "employed to live\n",
      "trying to keep\n",
      "try to cut\n",
      "going to wait\n",
      "started to slump\n",
      "started to run\n",
      "goin' to kill\n",
      "fighting to hold\n",
      "started to slump\n",
      "forgot to aim\n",
      "started to raise\n",
      "seem to tell\n",
      "want to see\n",
      "meant to insult\n",
      "meant to say\n",
      "tried to step\n",
      "tried to break\n",
      "seemed to pull\n",
      "seemed to change\n",
      "want to know\n",
      "start to work\n",
      "straining to stay\n",
      "Go to sleep\n",
      "stop to graze\n",
      "bothered to speak\n",
      "attempted to salvage\n",
      "seemed to indicate\n",
      "attempted to form\n",
      "tried to pick\n",
      "appeared to disapprove\n",
      "expected to find\n",
      "decided to see\n",
      "go to look\n",
      "wished to prepare\n",
      "hoping to see\n",
      "cease to feel\n",
      "forced to admit\n",
      "expect to see\n",
      "liked to liberate\n",
      "relieved to see\n",
      "try to thank\n",
      "like to starve\n",
      "hoped to succeed\n",
      "hated to answer\n",
      "got to get\n",
      "swung to see\n",
      "offered to walk\n",
      "wanted to avoid\n",
      "dared to enter\n",
      "failing to understand\n",
      "pause to consider\n",
      "chose to ignore\n",
      "dare to face\n",
      "continued to move\n",
      "started to brush\n",
      "trying to focus\n",
      "tried to fling\n",
      "want to tell\n",
      "attempted to kiss\n",
      "going to listen\n",
      "going to listen\n",
      "want to miss\n",
      "goin' to say\n",
      "going to take\n",
      "supposed to walk\n",
      "waiting to catch\n",
      "like to bother\n",
      "attempted to push\n",
      "going to kill\n",
      "going to kill\n",
      "trying to keep\n",
      "seeming to like\n",
      "got to take\n",
      "going to need\n",
      "want to put\n",
      "hoped to catch\n",
      "went to look\n",
      "wanting to unlock\n",
      "amazed to see\n",
      "refused to give\n",
      "tried to lift\n",
      "forced to agree\n",
      "fails to substantiate\n",
      "wants to see\n",
      "beginning to get\n",
      "continued to smile\n",
      "turned to add\n",
      "wanted to get\n",
      "continued to snort\n",
      "started to reach\n",
      "beginning to recover\n",
      "going to talk\n",
      "like to kill\n",
      "going to hear\n",
      "wanted to hear\n",
      "came to investigate\n",
      "managed to duck\n",
      "began to focus\n",
      "trying to yank\n",
      "began to snort\n",
      "wanted to show\n",
      "tried to start\n",
      "going to let\n",
      "going to fight\n",
      "attempting to speak\n",
      "longing to catch\n",
      "rejoicing to think\n",
      "like to get\n",
      "tried to go\n",
      "seemed to make\n",
      "aim to give\n",
      "want to trade\n",
      "beginning to turn\n",
      "forced to maintain\n",
      "hope to locate\n",
      "paused to gather\n",
      "bothering to note\n",
      "turned to survey\n",
      "started to struggle\n",
      "started to return\n",
      "pretended to give\n",
      "served to increase\n",
      "determined to drive\n",
      "try to go\n",
      "began to run\n",
      "continued to come\n",
      "continued to camouflage\n",
      "Forced to realize\n",
      "trying to grab\n",
      "trying to read\n",
      "began to drink\n",
      "started to slide\n",
      "mean to pry\n",
      "going to sell\n",
      "pleased to hear\n",
      "starting to itch\n",
      "like to think\n",
      "supposed to meet\n",
      "wait to get\n",
      "seem to dwell\n",
      "feared to dwell\n",
      "tried to date\n",
      "trying to cut\n",
      "beginning to collect\n",
      "determined to find\n",
      "put to use\n",
      "determined to spend\n",
      "hoping to escape\n",
      "pleased to note\n",
      "calculated to glamorize\n",
      "began to explain\n",
      "want to see\n",
      "wanted to turn\n",
      "began to whip\n",
      "failed to find\n",
      "meant to save\n",
      "wanted to waste\n",
      "want to get\n",
      "going to explode\n",
      "seemed to disintegrate\n",
      "intended to propitiate\n",
      "embarrassing to see\n",
      "going to keel\n",
      "seemed to enjoy\n",
      "like to know\n",
      "deserve to lie\n",
      "want to see\n",
      "pretending to joke\n",
      "used to try\n",
      "trying to look\n",
      "began to watch\n",
      "want to go\n",
      "seemed to sink\n",
      "began to ooze\n",
      "began to move\n",
      "seemed to know\n",
      "trying to talk\n",
      "continued to lash\n",
      "turned to look\n",
      "struggled to control\n",
      "deserved to live\n",
      "like to hunt\n",
      "left to say\n",
      "like to recognize\n",
      "wanting to fly\n",
      "dispatched to harry\n",
      "started to buckle\n",
      "began to crawl\n",
      "wanted to lose\n",
      "going to show\n",
      "going to give\n",
      "trying to size\n",
      "seemed to promise\n",
      "hate to run\n",
      "turned to face\n",
      "wish to enter\n",
      "intend to speak\n",
      "determined to make\n",
      "want to upset\n",
      "resolved to make\n",
      "left to distract\n",
      "vied to knock\n",
      "equipped to die\n",
      "tried to roll\n",
      "commenced to weep\n",
      "began to uncap\n",
      "going to get\n",
      "expected to risk\n",
      "decided to set\n",
      "seem to locate\n",
      "allowed to come\n",
      "allowed to leave\n",
      "sent to clean\n",
      "proceeded to disturb\n",
      "came to teach\n",
      "wait to get\n",
      "get to school\n",
      "beginning to stir\n",
      "got to get\n",
      "seemed to think\n",
      "trying to get\n",
      "struggling to get\n",
      "mean to pull\n",
      "got to take\n",
      "trying to think\n",
      "seeming to scream\n",
      "sought to make\n",
      "proceeded to give\n",
      "advised to flee\n",
      "like to hurt\n",
      "hurt to beat\n",
      "want to go\n",
      "chanced to glance\n",
      "vowed to take\n",
      "started to move\n",
      "began to sizzle\n",
      "cared to see\n",
      "time to pay\n",
      "determined to hold\n",
      "beginning to fold\n",
      "wanted to smoke\n",
      "seem to get\n",
      "trying to find\n",
      "like to keep\n",
      "seem to snap\n",
      "like to think\n",
      "beginning to find\n",
      "beginning to look\n",
      "going to last\n",
      "going to prove\n",
      "hoped to die\n",
      "gone to live\n",
      "stayed to get\n",
      "turned to go\n",
      "going to see\n",
      "going to laugh\n",
      "tried to bite\n",
      "seem to rise\n",
      "come to see\n",
      "got to know\n",
      "seem to take\n",
      "beginning to creep\n",
      "seemed to rain\n",
      "like to hear\n",
      "come to make\n",
      "started to move\n",
      "bent to pick\n",
      "permitted to operate\n",
      "beginning to get\n",
      "seemed to think\n",
      "tried to make\n",
      "wanted to present\n",
      "expected to stay\n",
      "wish to start\n",
      "got to run\n",
      "like to talk\n",
      "disappointed to find\n",
      "tried to reason\n",
      "trying to close\n",
      "want to help\n",
      "surprised to see\n",
      "trying to find\n",
      "neglected to play\n",
      "wanted to call\n",
      "like to offer\n",
      "want to say\n",
      "wished to see\n",
      "overheard to say\n",
      "like to get\n",
      "expected to perform\n",
      "going to bring\n",
      "seek to storm\n",
      "used to defend\n",
      "shocked to find\n",
      "hesitate to speak\n",
      "beginning to study\n",
      "grow to devote\n",
      "wish to turn\n",
      "going to fail\n",
      "wished to change\n",
      "wanted to take\n",
      "wanted to bring\n",
      "like to know\n",
      "intend to marry\n",
      "began to talk\n",
      "used to play\n",
      "trying to get\n",
      "happen to drive\n",
      "hating to get\n",
      "try to walk\n",
      "left to believe\n",
      "tried to rest\n",
      "dying to defend\n",
      "used to kid\n",
      "wanted to paint\n",
      "going to organize\n",
      "try to paint\n",
      "used to hang\n",
      "prepared to worship\n",
      "want to stir\n",
      "beginning to gather\n",
      "tried to believe\n",
      "used to say\n",
      "began to shudder\n",
      "come to understand\n",
      "come to see\n",
      "called to say\n",
      "decided to cremate\n",
      "want to meet\n",
      "impelled to kneel\n",
      "trying to touch\n",
      "trying to flatter\n",
      "trying to worry\n",
      "trying to worry\n",
      "want to go\n",
      "like to take\n",
      "wanted to know\n",
      "trying to get\n",
      "gone to purify\n",
      "waiting to see\n",
      "come to skirt\n",
      "trying to make\n",
      "go to sleep\n",
      "tried to emulate\n",
      "began to pulse\n",
      "amazed to find\n",
      "kneeling to tie\n",
      "trying to get\n",
      "trying to smile\n",
      "seemed to float\n",
      "tried to see\n",
      "stop to analyze\n",
      "supposed to joke\n",
      "supposed to handle\n",
      "want to ask\n",
      "got to admit\n",
      "tried to leave\n",
      "began to walk\n",
      "supposed to stay\n",
      "going to tell\n",
      "going to get\n",
      "intended to make\n",
      "began to zip\n",
      "obliged to roll\n",
      "want to stop\n",
      "going to marry\n",
      "liked to hear\n",
      "tempted to tell\n",
      "seemed to mark\n",
      "tried to explain\n",
      "managed to look\n",
      "needed to get\n",
      "answered to find\n",
      "afford to get\n",
      "started to look\n",
      "takes to get\n",
      "going to get\n",
      "tried to quiet\n",
      "trying to sound\n",
      "came to meet\n",
      "seemed to focus\n",
      "want to talk\n",
      "want to see\n",
      "wants to get\n",
      "went to turn\n",
      "surprised to find\n",
      "want to stay\n",
      "going to make\n",
      "hoped to dig\n",
      "trying to make\n",
      "going to lug\n",
      "surprised to see\n",
      "stop to read\n",
      "intended to move\n",
      "rising to sting\n",
      "arranged to live\n",
      "managed to find\n",
      "inclined to wobble\n",
      "supposed to care\n",
      "shuddered to think\n",
      "seemed to understand\n",
      "aroused to go\n",
      "hate to call\n",
      "wish to leave\n",
      "seemed to follow\n",
      "supposed to matter\n",
      "bothered to tell\n",
      "hesitate to use\n",
      "used to say\n",
      "try to persuade\n",
      "wanted to keep\n",
      "trying to keep\n",
      "blushed to admit\n",
      "want to continue\n",
      "chosen to represent\n",
      "want to leave\n",
      "wanted to work\n",
      "seemed to regard\n",
      "forbore to mention\n",
      "managed to open\n",
      "want to encourage\n",
      "want to watch\n",
      "remember to warn\n",
      "used to take\n",
      "want to use\n",
      "started to throw\n",
      "began to swing\n",
      "started to carry\n",
      "began to look\n",
      "going to happen\n",
      "began to walk\n",
      "trying to say\n",
      "trying to say\n",
      "started to say\n",
      "wants to take\n",
      "started to take\n",
      "began to fascinate\n",
      "left to spend\n",
      "seemed to work\n",
      "work to grow\n",
      "trying to talk\n",
      "lied to shorten\n",
      "trying to make\n",
      "wanted to force\n",
      "refused to take\n",
      "seemed to please\n",
      "grew to depend\n",
      "meant to tell\n",
      "trying to pull\n",
      "seemed to shiver\n",
      "trying to remember\n",
      "worked to recall\n",
      "wished to call\n",
      "seemed to stare\n",
      "began to tremble\n",
      "refusing to think\n",
      "refusing to think\n",
      "hoping to frighten\n",
      "wanted to run\n",
      "began to ache\n",
      "began to bother\n",
      "tried to take\n",
      "wanted to kill\n",
      "stopped to see\n",
      "going to tell\n",
      "going to push\n",
      "wanted to slap\n",
      "started to type\n",
      "helped to build\n",
      "refused to drive\n",
      "wanted to go\n",
      "likes to play\n",
      "like to rise\n",
      "tried to push\n",
      "trying to run\n",
      "tried to make\n",
      "used to say\n",
      "come to exist\n",
      "fit to put\n",
      "stoop to lift\n",
      "wanted to draw\n",
      "going to pick\n",
      "intended to wait\n",
      "determined to foil\n",
      "strode to answer\n",
      "trusted to carry\n",
      "seemed to help\n",
      "chose to come\n",
      "tried to ignore\n",
      "liked to break\n",
      "began to aid\n",
      "going to tear\n",
      "like to exhibit\n",
      "forced to make\n",
      "hate to admit\n",
      "got to decide\n",
      "tried to sell\n",
      "try to swing\n",
      "expect to call\n",
      "paused to get\n",
      "proceeded to search\n",
      "began to suspect\n",
      "endeavoring to cut\n",
      "wanted to know\n",
      "daring to commit\n",
      "given to dig\n",
      "urged to attend\n",
      "refused to receive\n",
      "beckoned to cross\n",
      "taken to keep\n",
      "trying to hold\n",
      "managing to get\n",
      "promised to illustrate\n",
      "pretending to black\n",
      "wanted to remind\n",
      "Resolving to get\n",
      "started to start\n",
      "starting to woolgather\n",
      "bound to get\n",
      "wanted to keep\n",
      "looked to see\n",
      "chosen to complement\n",
      "live to hear\n",
      "leaping to light\n",
      "trying to prove\n",
      "trying to determine\n",
      "supposed to put\n",
      "proceeded to neglect\n",
      "care to count\n",
      "shuddered to think\n",
      "telephoned to announce\n",
      "neglected to consider\n",
      "trouble to memorize\n",
      "deigned to appear\n",
      "seemed to understand\n",
      "try to revive\n",
      "seemed to produce\n",
      "returning to jump\n",
      "tried to farm\n",
      "going to set\n",
      "turned to stare\n",
      "seem to think\n",
      "liked to hire\n",
      "remember to telephone\n",
      "like to work\n",
      "like to disclose\n",
      "got to put\n",
      "got to run\n",
      "waiting to get\n",
      "need to worry\n",
      "seems to think\n",
      "tries to baffle\n",
      "mean to reconsider\n",
      "refused to make\n",
      "seems to make\n",
      "seems to refer\n",
      "seems to say\n",
      "delighted to meet\n",
      "like to know\n",
      "regret to say\n",
      "seems to refer\n",
      "come to talk\n",
      "seem to remember\n",
      "want to hear\n",
      "trying to get\n",
      "forgot to say\n",
      "seemed to believe\n",
      "wanted to get\n",
      "wanted to see\n",
      "wanted to touch\n",
      "got to entertain\n",
      "tried to keep\n",
      "going to tell\n",
      "thought to mix\n",
      "going to jump\n",
      "beginning to catch\n",
      "try to see\n",
      "phoned to say\n",
      "bothering to look\n",
      "forced to wipe\n",
      "used to pretend\n",
      "refused to approach\n",
      "used to express\n",
      "proceeds to lash\n",
      "used to hang\n",
      "seeks to expunge\n",
      "trying to redeem\n",
      "seemed to take\n",
      "tried to conceal\n",
      "came to know\n",
      "refuses to continue\n",
      "continue to scrape\n",
      "given to understand\n",
      "propose to vent\n",
      "proceeded to mask\n",
      "withhold to keep\n",
      "begin to wither\n",
      "help to intensify\n",
      "seems to overtake\n",
      "want to buy\n"
     ]
    }
   ],
   "source": [
    "from nltk.corpus import brown\n",
    "def process(sentence):\n",
    "    for (w1,t1), (w2,t2), (w3,t3) in nltk.trigrams(sentence): \n",
    "        if (t1.startswith('V') and t2 == 'TO' and t3.startswith('V')): \n",
    "            print(w1, w2, w3) \n",
    "\n",
    "for tagged_sent in brown.tagged_sents():\n",
    "   process(tagged_sent)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0faed4b",
   "metadata": {},
   "source": [
    "最后，让我们看看与它们的标记关系高度模糊不清的词。了解为什么要标注这样的词是因为它们各自的上下文可以帮助我们弄清楚标记之间的区别。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4dc7d02f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best ADJ ADV VERB NOUN\n",
      "close ADV ADJ VERB NOUN\n",
      "open ADJ VERB NOUN ADV\n",
      "present ADJ ADV NOUN VERB\n",
      "that ADP DET PRON ADV\n"
     ]
    }
   ],
   "source": [
    "brown_news_tagged = brown.tagged_words(categories='news', tagset='universal')\n",
    "data = nltk.ConditionalFreqDist((word.lower(), tag) for (word, tag) in brown_news_tagged)\n",
    "for word in sorted(data.conditions()):\n",
    "   if len(data[word]) > 3:\n",
    "       tags = [tag for (tag, _) in data[word].most_common()]\n",
    "       print(word, ' '.join(tags))\n"
   ]
  },
  {
   "attachments": {
    "e9d9a0887996a6bac6c52bb0bfaf9fdf.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "a1c03b99",
   "metadata": {},
   "source": [
    "注意\n",
    "\n",
    "**轮到你来：**打开词性索引工具`nltk.app.concordance()`并加载完整的布朗语料库（简化标记集）。现在挑选一些上面代码例子末尾处列出的词，看看词的标记如何与词的上下文相关。例如搜索`near`会看到所有混合在一起的形式，搜索`near/ADJ`会看到它作为形容词使用，`near N`会看到只是名词跟在后面的情况，等等。更多的例子，请修改附带的代码，以便它列出的词具有三个不同的标签。\n",
    "\n",
    "## 3 使用Python字典映射单词到其属性\n",
    "\n",
    "正如我们已经看到，`(word, tag)`形式的一个已标注词是词和词性标记的关联。一旦我们开始做词性标注，我们将会创建分配一个标记给一个词的程序，标记是在给定上下文中最可能的标记。我们可以认为这个过程是从词到标记的映射。在Python中最自然的方式存储映射是使用所谓的字典数据类型（在其他的编程语言又称为关联数组或哈希数组）。在本节中，我们来看看字典，看它如何能表示包括词性在内的各种不同的语言信息。\n",
    "\n",
    "\n",
    "\n",
    "<a href=\"#31-索引列表vs字典\">3.1 索引列表VS字典</a>\n",
    "\n",
    "<a href=\"#33-定义字典\">3.3 定义字典</a>\n",
    "\n",
    "<a href=\"#34-默认字典\">3.4 默认字典</a>\n",
    "\n",
    "<a href=\"#35-递增地更新字典\">3.5 递增地更新字典</a>\n",
    "\n",
    "<a href=\"#36-复杂的键和值\">3.6 复杂的键和值</a>\n",
    "\n",
    "<a href=\"#37-反转字典\">3.7 反转字典</a>\n",
    "\n",
    "\n",
    "\n",
    "## 3.1 索引列表VS字典\n",
    "\n",
    "我们已经看到，文本在Python中被视为一个词列表。链表的一个重要的属性是我们可以通过给出其索引来“看”特定项目，例如`text1[100]`。请注意我们如何指定一个数字，然后取回一个词。我们可以把链表看作一种简单的表格，如 [3.1](https://usyiyi.github.io/nlp-py-2e-zh/5.html#fig-maps01)所示。\n",
    "\n",
    "![e9d9a0887996a6bac6c52bb0bfaf9fdf.png](attachment:e9d9a0887996a6bac6c52bb0bfaf9fdf.png)\n",
    "\n",
    "图 3.1：列表查找：一个整数索引帮助我们访问Python列表的内容。\n",
    "\n",
    "对比这种情况与频率分布（[3](https://usyiyi.github.io/nlp-py-2e-zh/1.html#sec-computing-with-language-simple-statistics)），在那里我们指定一个词然后取回一个数字，如`fdist['monstrous']`，它告诉我们一个给定的词在文本中出现的次数。用词查询对任何使用过字典的人都很熟悉。[3.2](https://usyiyi.github.io/nlp-py-2e-zh/5.html#fig-maps02)展示一些更多的例子。"
   ]
  },
  {
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BCEAAAhCAAAQgAAEIQAACEIAABCAAAQhA4HUCS8ABAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBgBFhVZigQIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQOCPWFVGI4AABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBxQRYVbaYBRIEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIsKqMNgABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACiwmwqmwxCyQIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQYFUZbQACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEFhNgVdliFkgQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgwKoy2gAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEILCbAqrLFLJAgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAgFVltAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQWEyAVWWLWSBBAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAKvKaAMQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgsJgAq8oWs0CCAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAVaV0QYgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAYDEBVpUtZoEEAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAqwqow1AAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAwGICrCpbzAIJAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABFhVRhuAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgcUEWFW2mAUSBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCLCqjDYAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAosJsKpsMQskCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEGBVGW0AAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABBYTYFXZYhZIEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIMCqMtoABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCCwmMHOxOKHS448//uSTTw6rcEsttdQKK6zwhje8YcaMGfXSXLRo0QUXXLDccstZ9Oeee27nnXfeYIMNTIMAgQIBNZt777136aWXLujXX3/92k2xkFT08NVXX73rrrsK511xxRVXXnnlqD1KCEAAAhCAQIrAPffckwqqpJ89e7acsWWWWaZSLIwnj8AUeikph1CVO3T3LDqGWmONNfxRzOQ1KkpUg4AGsw899FCNiNEoL774ooYeus/rb9lllx3pSCeaAZRNCIQdvepx9dVXb5JmP+NOYQfUz4ogVxCAAAQgAAEITCqB6IBUhV111VWXX375JqV++OGHn3322TAFPfhbZ511Qn15zdNPP/3oo4+G9s1TDtOcPM0NN9zw85//vDDhsM8++4yupKk2NqIz0gxGBJZkIdATAvfff/9LL73UbWbaX2dy5ZVX3nfffW3euguEp2caqlDwrg5nvPbaa12du53zavnLE088MfRz6eI88MADDznkkKq+ppr4hhtuWMjP8ccff8IJJxSUHELACJx88snHHnusHZpwxx13zJkzxw6HLkSb60orrSSfe+jnIkEIQAACEJhgAtEOpWF5t9xyy0MPPXTvvfeeyCfWDeFMQ/Roo5psLyXlELrq1uTFzJlDe2UoOoZizDINV1bVMuabZdXUCvYadO+1114ad2+22WasMCvA6dvhVN2Tp6qwfWtp5AcCEIAABCAAgWkgoCdo8rjCku64445XX311qC+vyQwrGj4qPeyww84666xoThqmHE1zwpTRGn/llVeWWGJUn9uKTnqMlOpIizPSnJM4BCCQJ6AXz5Zccsm8TQuh7d9k2r91+xiZmfFptCOPqktuJ/dlzqLNnMqYVbVRYz3xxBPXXXddOR9aR18+utakh8Z6hTdUooGAEUi1kMIuYmY/LCHaXEd0TQ0rz6QDAQhAAAI9JBDtUBrm85ZbbjniiCO0eZLmBM844wxt49QwQaKPF4Foo5psLyXlELqK+8AHPjDEGoySzGdgiGcnqTEiMNJWoUH3F77whS222ELPEvbff//og6UxYjXZWZ2qe/JUFXay2y2lgwAEIAABCECgnwT0RlM0Y9dcc01UX1KpjZYzlg1fpL/ooouiiet9yKgepU9A+6/7hy3I0UmPFs7LKSAAgckjkFmvPHmF9UvU/q3bPzszMz6NduTJX1U2ao7aCG2bbbbZaqutXn755VGfi/Qh0AcCQ/ykbB+KQx4gAAEIQGACCGh52axZs4455hheAJ2A2mxShGn2Ui677LJbb721CT3iQqDPBC644AK9BMm4u891FOZtqu7JU1XYsK7RQAACEIAABCAAgSES2G233VKp5VeGpWI5/VVXXZUxuPjiizOh+SA9HEx9MUnfO8rHJRQCEIAABCAAgaEQYGZmKBhTiQztIympE0yJXrtlaFGkPh9b9YOYU8KHYkJg1AS0e0F4ioULF4ZKNBCAAAQgMKkEtKWN/vDHJrV+KddAAtrSiYWVAylhMNYE3Lj7qaeeWn755ce6IGS+DwQYQvahFsgDBCAAAQhAAAIQCAmsttpqodJpbrvttq233joVmtenvlDpYn3pS1866KCD8imkQu++++5U0Oabb54KQg8BCEAAAhCAAATGhQCryoZZU/og5q9//esNNthgmImSFgQgMIiAvlqt3QtCq69//ev6XE6oRwMBCEAAAhNMQP7YFVdcMXfu3AkuI0WDQIrACa//pULRQ2AyCKywwgrPPvvscsstNxnFoRSdEGAI2Ql2TgoBCEAAAhCAAATKENDXxHbcccfo9y7/9V//td6qMr2CpR2+M2fXGyxyEes9TUjtc7bSSists8wymZMSBAEIQAACEIAABMaCAOsthlxN+ioHn8IcMlOSg8AgAlP71epBYAiHAAQgMKUEdtlll0svvXRKC0+xp5vAiSee+Pjjj083A0o/FQRmz549FeWkkCMjwBByZGhJGAIQgAAEIAABCAyBwBFHHBFN5atf/WpUP1D5wAMPDLT5xS9+MdAmanD++edH9UceeWRUjxICEIAABCaGAB+OmJiqpCB5AlO6V5leEdAmFi+88EKejgudNWuWLOV06rPrF110Uer76JbURz/6Ub67ZzQQxprASy+9FOZ/4CUQRkEDAQhAAAIQiBLQu6ennXbawA/emzP2k5/85Fvf+tY999wTTc1Xvv/977/22mu33357X4k8SQTwUlK1+da3vlXbJ6dC0UOgHQKXXHLJWmutVXLE7bKkW72EBx988Oqrr9azooGDDn2+kHF3O7XJWQoE6IAKQDiEAAQgAAEIQAACQyeg1wWjaWqYoG0dZs6s/GSzzNjh3HPPPeWUU6LnzSi1nkD7nEUNop9cj1qi7AmB3XfffUQ5qbcN3ogyQ7IQgMAQCejqPv3002skmFo/ffTRR2+00UZVE+QmU5UY9lUJVPa9qp6gn/brr79+jW1y99lnnzPPPPO5557T2rKDDz44VTR9iU+3j5VXXjllgB4C40Jg1VVXPfTQQ/VAyDL8zDPPbLvttnaIAAEIQAACEGhCYN999918883LpyBnTBN8+iTBTTfdpPFV9GsIltoOO+zAp8mNxuQJeCmpOtWyS+3Vt8cee6QM0EOgBQJNWqDi6lavcbeG1ccee2wqtxp3a3i+/PLLpwzQQ2BEBOiARgSWZCEAAQhAAAIQgIARyDxfu/vuu+fMmWOWJYUzzjhjoKVebqmxquyRRx5JpVxjWUAqKfQtENhyyy359EELnDkFBCaPwLx582oU6qyzzoouSq7RE9U4O1EgUJXAlK4qG7glRobjcsstd9BBB2lDsje96U2prTIUivORYUjQuBDQQxo9qhmX3JJPCEAAAhAYOwLPPvtsjTzrzRu9HqDNbLTmYNddd82sLdOnybWhSI13WGvkiigtE8BLyQDXXn20/AwfglogoOW/Dd+S1Lh7/vz5hx9++AorrJDK8D//8z/LJhWKHgIjIkAHNCKwJAsBCEAAAhCAAAR8Avvtt5/eJPE1Tr744ourjgK0vVn4LE9fNCpskKzDp59+uuqLK9/73vfCTEqzwQYbMB8VJdNbZZMHx70tFBmDAATGjkDzKbWxKzIZHgsCS4xFLnuYSU2RZ74sc9lll+ma72G2yRIEIAABCEAAAhCYGAJac6C1ZWeffXamRB/4wAcyoQRBYFIJ0PIntWanrVx6ovPYY4+lSn3SSSelgtBDAAIQgAAEIAABCEAAAmNN4K/+6q+i+T///POj+ozypz/9aSE0XFLmDL7//e8XLAcennfeeVGbqkvfoomghAAEIAABCEAAAn0gwKqyRrWQmeD+zW9+0yhpIkMAAhCAAAQgAAEIlCCgTWSvuOKKlKHW+j/88MOpUPQQGGsCmgdP5V8t/84770yFoofAGBHQt28uvPDCaIbdXgLRIJQQgAAEIAABCEAAAhCAwFgT2G677aL51/fCXnvttWhQSvnFL36xEFTYpcxCa3x3TKNvi+4Le+65p3+IDAEIQAACEIAABMaXAKvKGtWdJrh33333aBLf+c53onqUEIAABCAAAQhAAALDJTB37tzTTz89leaHPvShVBB6CIw1Ac2D65sa+ouWYpNNNonqUUJg7AjsvffeqTw/+uijqSD0EIAABCAAAQhAAAIQgMD4EtD+9KnMP/LII6mgqD76Jc3ox4iuueaaSkvW9MXM6BmlXHvttVNB6CEAAQhAAAIQgMB4EWBVWdP6Ov7446NJXHfddVE9SghAYOgEKo30hn52EoQABCAAgT4QmDdvXmqtv+YEM9N8fcg8eYBAbQLrrbfeTTfdlIp+wgknpILQQ2CMCMyYMSN1hx+jUpDVXhFgCNmr6iAzEIAABCAAAQhAIErg0EMPjeq/973vRfVR5eOPPx7V6wWtLbfcMgx64IEHQmVKo43TokE77rijRjHRIJQQgAAEIAABCEBg7AjMHLsc9y3DW221VTRLP/rRj6L68krNct57773a80yOqRzZ5ZdfXg9E9ftnf/Zn73rXuzbffPNlllmmfGplLO2M3/3ud3/+859r84P1119fedC3dfS8SvP473vf+zbddNOZM0febF5++eW77777hz/84T333PPMM88o8294wxvWWGONHXbYYc6cOUMvuIOzaNEifSfoxhtvdCcVc51LQ4u3ve1toztpmXqZDJtXX31Vn4X9yU9+ctVVV91+++1WqBVWWEEbaWy77bbvfOc79fpOvbHWEkvEF8jWS83yhgABCEAAAuNF4Jvf/OasWbOief7CF75Qe3lNfxwkFU1f89Tk6W233eYcJGk22mgjbdX25je/uWSvp+lUeTs333zz7373O8dqzTXXfMc73qG5VPmZUXq1lUInh+oHP/iBHAAJzpV1K/zU+2+22Wb6noXy34JvWaMInbijNfIp/0rbJx999NFq5GH0E0888cgjj5RBGNSypj+NYbwuIldNjFPEYdddd41+Wab5ouGW8Y50WJS5rnvVl2Xy2VpQf4aQvaqacen7WmsnnAgCEIAABCAAgW4JHHLIIWeddVaYh/POO2+fffYJ9VHNxRdfHOrderWjjjrq4IMPLoQuXLhw/vz5BWXqUDmJBoXJRs0Kyv54hnILf/zjH1955ZWafXLP5g466KDM7nGFguhQI7UHH3zwoYcecqv6ll12WT1kXHfddSslEiY7nZrm1SFuLY98rab8IYaahPSaftSU4M4776wH0CUnMy21sRO6wl4A5ddCC8/cVerrr7/+3//933UP0RoDVfSTTz6pJ+9abLDFFltosYE2pE99eKGQcw4bEujDfKzy8Ktf/UqNQfsx3X///a5E66yzzuiWuzSB1pNrtkkRRhhXdTnZf9G3DXS3GlapNSkcrZ7UKe67777QfsGCBX5+nnrqqdRLGH5cneKKK67wI9aWtdnvfvvt5yeekYVU/UHVcz322GPKsF7RsD+lI6WfjmBecsklMsucXUEykJmM/bi1ZdEWf3Vm+ZPKQJ/WeuGFF2qfqEnE1Fe9VHFNkh0Y99lnn9VufOLj/wn+wIi+gVpL9DKMApc/8bOf/cyPHpULzSmVvvTW3iSETS6aOEoIQAACEBg6gaj/o45A/ctwz6W1NdH+RcoaJ2rBQVKuCp2a67AKPt4rr7wiVqmiOb28x4yj4vr0fAoKPfvss3WuGqwKUdSbl99YSK7dhRde+NJLLxUSyR/W8FKiqPvgjuZLaqFRh1D0ZJAakqhOnYElUlKIOlf1LtgWGkO0Zsf9IlJNjfU4ZSh3Er+56u4UvYPVa5ad4B3FsMhHlJLb6cuiHX3m/nPttdeGg7WqI01X5GhSUhaAFG4U0buc2liYq0I3oWRrdECFzLjDdqqmUPDouLiTqZgoE5QQgAAEIAABCEDAJ6BZjugoQErfLC9HHT/3DEKeUph+xokNT5R6rqTVVKFxRtOOZyg/NnR3C56zJmeiTyfvuOOOTP4tqExB9MhJObEoEqJ1NPRB5cAzVqp6P7V6cgvVoYx1OLFQZgisB9OFAZfG+P5DPSenHgdHB4MjHVeWrOsWsPdzoFem0u2uq1tBZipbkwy6JAuNQfcKzQ6VrIWoWSFBd6gHClHjUSujtz7xGcrdr4X52Gj+/cyrlZZZfKKKLsziliFfdRoqn2YL12w+A2MRWsH3GovyhJmMtukhOgfysewO6AupU0RbuU2Fq2ssc4H5J5LsPOCw7GU0ruMpJFjysNJ5owWXi2mZVE9f8rxmVuMuY6eTIKdNHYalVlIIvRw/zRHJ0YeIyrAPcBSnVvohFo2USp5LY6fUsCpMtqDJFy3anAopRA/zyZYsF2YQgAAEIFCVQOq+bS5Q1QRT9nJsovd/KUvOf7mUW3OQdLooHJ+MVlylChXq5TOEcKITgmFc09Sbf3HnlXtm6VQV5GVpCBfmP6qp4aVEUfu+QfvuaLRoKWXUIbRBhzzzFPAaFRodQ/nNMpVJX99aY4jWrJ/b8bqIxHACxin+LJLfKmrLqVVl/iVcMvH28Y5uWJQvcud9md2gwnxGb2j+ZRtGSWlKJhW9UaRum74+bGM1OqBC5juvGr9QPe/7Cug4hAAEIAABCEBg2gikni+UnD3QwMR37Uy2F9tM4wuZhQ4+/6EsemvTM4z6sb4TrhUhPgdf9h1IH4LJmpRIVZafjsmaArKlQtEpiKEPKi2rEqJnzIxf/LjDkkdaHcpk+yNfI6M5WKvoMoIag1W3aiGMYqF2CieUHAwWYkUPh5VUa9ijI1z/Om15oBdtz2FVhhqtLbNbgV81ujeGxk7jm1WSU5mcsFVlrc3HRm+k7mqtt9xFfVD5Co1eAjVu461ds+WL1lvL+GfjUhcq+pDAkksuGSqlWXHFFaP6jFK76ukrDBdccEHGJhqkHSO1bWA0KK+89NJLV1llFW07mTdLheq8+++/fyq0vF6Xx0477fT+97+/fBRnucsuu5xzzjlVYzn7iy66aPbs2ddcc03V6KogQav9Fa2qp+vWfqmllgozUHJsoKrRR0v1HdUwhTKaDTfcsHbllkkfGwhAAAIQmEgC+tJfdPZBhY1+HzAKoScOksvbHnvsse+++0bzGVUeccQRvnsm91KbyUc/GBGN7pTyymr0wvqwoM4l9yyTcj5IXpY+jV3Sy2ripYTZ6MQdDbPRRLP55pun9odThWqv+yaJV43bcmPIZ2+MLiJXEMYp0Qp97rnnovqqyvbxdjUs6lVfVrWaem7fsAPqT9VMQN/X86ZC9iAAAQhAAAIQaE7gyCOPjCZyyy23RPUF5V133VXQ6FAPwmfOnOn0Ws4SGmQWV/nGd955p39ocmpsbgYmtOwZRv1Yy8zJJ5+8zTbb2GElQTNRelxY6WGQpoD0PFQfyqx0okkyHl11iFL7I1+rGk3rbbLJJnZYRlBj0KN297HUGs/Wy5yiHZsOsfsFbH+gp7uHHun6eSgvn3jiiboVhAsVMpP8rqmUP4VZ/v3f/73JvvCZz3zGPxxfuQ/zse7GXm+5i/qgww47rE3+Pblm2yxyk3OxqqwJvd/Hff7556NJ6CPBUX1UqeVNWrY5a9asaGgZ5Q477KCbRRlLsznmmGNqLOSy6E5QZ6/Hh7UfU8ltUsF1c6mxustlQN+nv/XWWwu5GngoH7fSA9owQfVztfvIMLXJ06h1qWoalkspnHHGGQ0TIToEIAABCEwbgc9//vPRImuQENUXlH1wkJQlOYf63WqrrS677LJCDgceyj1zhdWSstrupXrhcDyfObUcUa0mzxiUD5KXpVoob9/Qsit3tGG2o9G//e1vR/VSfuADH0gFDV3fk8YwdheRqwjGKakGqe2+UkHl9e3j7WpY1JO+rHzVTI9lT6pmkvq+6Wk8lBQCEIAABCAwnQTkw0cLft5550X1BWX0LbujjjrKzObNm2eyCSWfSvznf/6nRfGFkhvG98QzdDnXopBjjz3WL0V5WZNXmokqb+9b6t1CvUH0xje+0VciN6kO0Wt/5GtVplNrWs8OKwnazkMzmeO7qqxD7Ma5k4GeXuasffewnOuBe7hO98tf/rIZ+MJXvvIV/7C8HO0RtJGKVrCVT6S3lj2Zj9UDBd3Ya1NSHalPqR29UsQ+XLOVMty5MavKmlbBbbfdFk3iwAMPjOqjSr1X8aY3vSkaVF5Z6UmeHNbyO3YMzIM6qnqvFGi3yVVXXXVg+nkDvQOhpWl5Gz9UPVxtH9dPR3fGyehp/EINRdYbNsNqXdpw5corrxxKrkgEAhCAAASmhMCuu+4aLanemBzoMPTEQXL518Cm5Ku3YXm1el4jyT/+4z8Og8prNJ7Xy21l7GVWyREdmKYciXob8Q5MOTToxB0NszEUjV62Tu1vr+WJ4ezMUE5aSKRXjWGMLiKHkXFKoTn5h//4j//oH5q81lprmZwX2sfb1bCoV31ZvlKmLbQ/VTNJfd+0tSLKCwEIQAACEJg2AhtttFG0yCVfHfzqV78aRt9tt91MGd2dq+Tzo9Tis5133tnSTwn98QxXX311TRfUXhSizxDVnrxycPRKWI03KlNgx13fsDpU/PZHvsZcmwyVvHYsSkHQTKY+pVpQjsVhh9h9Pu0P9FTwYV2/2uKuMHWfmuQ/6aST/FKXlFOvT5966qklU+izWX/mY5vvyKM+pYWFZT25ZvvcqMK8/WGX1zAATUkC+tpu1HLu3LlRfVQZXR7rLLUZ71ve8pbll1/+9ttvH7ihl7zY6KsVhZNqnXt+0c+OO+6o1TybbrqpzqvlYr/85S/15ke+Y9DKU30rV7uOFc6VP9QWaxkDfcFqvfXW065vA71SubynnHJKJikL0rOlfEF00r322ktjFb0Nf+ONN+qJZmbbXgXJab766qstfQRtXJfZA0/tWW8Cve1tb1PTevHFFwX5Rz/6kWok07b1Ia2XXnrJtqR2hKUBNQQgAAEIQCBKQF1MVC/lgw8+uM4666RC++MgKYfyxFL5VGeq9/Y0yZJxURQ3v8zLfSc0NZy2U//TP/1TGd9y/vz5FiUU9MqXvlgh31hZWnrppeUA3HHHHer9Mw6wEpGXWMO3DM8+UNO+OzowS00MNCRW5UZrVrMzJZcJNslAfxrDeF1EYs44JdPw9HWD6B1PrX2ZZZbJRLSg9vG2MyyyAprQq77MctUHofMhZK+qZsL6vj40MPIAAQhAAAIQgMCICOi5QHSQqwGCXO7CU4NCHrQJVnQcoYU7ZqkBRTR9jUHyewpo9UN06K2UMxNT7ry98gwvv/zy2p+dUUEyT3YMspvI+s1vfpMiZpYITapD9Nof+VqVnXPOOfmJPk0PapHQ2muv/cADD+ixYLQxuAtWltEr187VN6FD7AUULQ/0tI5w4AN3TSz/yZ/8iT77du+99+afBassaJ+VGgAAIABJREFUBxxwwMKFC61QusNruUJ4k1HzGHiLtkRMSH3+MvodZIs1LkJ/5mNTxNQR6OnAmmuu+bvf/U67yeQXfihUa69LLvxInTGj7881m8lkH4P0aGGy/9RMQ+5yE4dS6vvuuy9M3GmeffbZ6CkyUfyk1Gtee+21ckwLiTz22GP5G1zBPjzUUmX/RAX57LPP1gO8MJY0yswVV1xRsPcP9bX4aESnLF9wneWFF14oJKWnj9GqtAyErAop6PD00083+1DQAsHwvIr11FNPHX/88aG9aRQanmu4mlTOVZvDPVEhtWit5S+f1PbOah4iWUjfDlV9qa01xDlKWE8F3J9arFK26vAF6RX6/w1//9/OiAABCEAAAm0SiHYoumMvWLBgFNnQgNPvDkxWX5M6XVcOkvKTgmPZdoIKJUs//+rXLrzwwoJZ/lBdaqE7VprqozOx/DNGZTm9qejKc+F0hRSEPePgyScs2PuHUW55LyUaJcy8/PDRuaN+EarKUYcwLHLKL1JJy19x0XoZGL1XjSGs2d5eRGoJ0cq1IozjOCU1qKza7J196q6u0WuZBDvB29qwyCfQt74svEFZbqOVMvAmY9F9oXxSNjBsOISM9iaZwiq3fasau72Y0Nu+z69rZAhAAAIQgAAEppOAvERzWnxBz4zyQKJPHPR8rRArmv7AsUbUJ1T2NBAopF847KFnKFfQB2uy9JockKMrjcpbKIj4m2VU0JxV4TnpwIeMfjrDHVQWMh+d9Mi79IUUmh+mmlC96lB+oiMjQzrSiQXtH2EnCgWN6GVQIKb61eRbqrCFRFKNIVrkUY8rCwWJ5sHyPzrsqfZjp3aCCI9okvP6668vnMs/1J2wcPk7broJaPGDb1mQC00ldRZhL1TEwMPCidyhbgUDI47OIHojUsZSDT6Vk67mY1P5L6BWZYVP5we2BCVSaAyF4kcvgTK38a6u2UL+x/Hwj8Yx05XyHG3TZVrVwLOoxRcuDDvMpB9t5RbRCZlHni5X6gMKUewwf40pulmGQnRNVcghs6xNDnFo7zRlCj7QU9ej0DDbTqP0U6d2+swDNqWg5Xr56LrlRduSO/tA7PnEB4am7nEZ4APTLGMQrbVM81ZXF60geU5lTpfp+cIux08wdTFW7Xr9NJEhAAEIQGCIBKIdirqMeuP8gRlL9ZuZ00X7L6ccqYOksqTg+FnKTJXmPRxLRDM4mYJk5jTzy8KU/9TKtuii8GjdyQO0fPpCjTcWMl5KSdQjdUejxS+vjDbsaJGjlo7twAp1+Yn6vZkryMXqVWPw25LkPl9E+at4TMcpQ/TDU7cIVWt+jOCaZSd4uxoWFZq9f5jpAvy7UL3BvlKI9mXRG5Q7XfQ2NfAm42fV5HpJNRlCVi2ssurXRUHupGoKeehz32cVjQABCEAAAhCAwNQSiHpf8mcGeo/Rl+i0oKFAMpq+BsUFs8JhapwSpl+IWPDE/MM+eIbKj2aQtEH+wDGdn/OCPLBq9MCoECU8HJiBAthKh9FJj4GVXukUA42jDS/kULI6Ohn5Whkzi8MGPu+Orv4scEg1hnqDQcu2L9RLqkPsZdrPSAd6hTryDwc+Mddkjm/vy4XZ4NTIXU3Or76Bcmrqe+Ade2DKTQyiNyLRSDX41Lm6mo9N5d8qVLU5sCyZaSjd/VJFlj56CWSmoVxSHV6zmbKMSxCrymrWVLSx2nWSuQ3lI+o+GF29G+Yytb4q30lklqOVmZG3bOhKtsL6QuZyzRdcichPtfQzQuompS4/E0tBqTxX8hRTZy/0c/mc1AiNOjSC1rdVZdHXUyr17qkbuhakZ7ilHIuB3VUmTYIgAAEIQGCIBFJuwMBppnp5SM1HpE7XoYOkAqbgOBdL3ehAJ02ep++PhfJAN0nZSEGQPl8LURdr4Lu5hTSjA8iMY5niViOKj2vU7mih1FUPow5hqsh+uXw5ZV/ITNTpTV1BFrdXjcFKPaYXkfI/1uOUofjhGhpHbw6ucsvc2dQ4o81y1Hg7GRalbuMq7MB+xK7iDLH83SPal2WiRG9oA28yfj5NrpdUkyFk1cL2sGrcReR+e973WUUjQAACEIAABCAwtQRSnlt+xJSKFa7cSlnmBzWpgUaYvl9xPfcM5R+WfOSUKYiC/CKn5NTcnXmqef6pZEvqo5Memj2Qb6xHUUP502RdvgjRYYUV3wklq0OlTjXI/GVSwBXFopzkH4AKWiHbdph5wc8/dSYFl1SKZL3BoH9qk+sl1SH2ge1npAO91DIm1Zce8hrVjKBbpbWTgqB7sh8xNS808CVMP5HUStZU0/Ljjk5OXXFVcxVthy1Mzqfy7yo0v17Fp5qq4nxzil4CmWkod8YoK52ohVulX+Qxlad0VVmlxlGoWvWC+etEja8QxT+MtnJ3gem3/J0i5enmiyavyM7lC8qVn8mBcursSjOVVL7gJX0LZSy16ihfcK2M9str8sD7SwGFKsjiFoTydVdIs8xh1KFRBsr7lGXOEtpEay0DLZrPqs8Gor17PpFUgxxppYS40EAAAhCAQIpAtENRR5a/vadSG6hPnU79VDRuhw6S8pPKrfM0CgPpaP6lLLgl/qEGZqlYBX2UQ76OUl1wyRkEy0Bqv9JM8aPcMl6KzhWNYqxG7Y5aYWsLUUcrVeRMYcvM8EbHO+PVGKxmM63IrwuzD4WRXkSTOk7x2VaV5cPretS0dVgXptE0UJlku8IbvVrzV1BYnKrDoug9XMR0NwgTz2hSN/Z8UtF7TuoGpbMPBZErRb2kUsUsM4SsWtgeVo1dSv3v+zJtlSAIQAACEIAABKaHQHSIKpcmM9yr5LNFRx/5NRnmUPmCHL98pfTZM1RB8kvi/KKlCjJwYyo/kczSNGWmjGfup1ZJTrUovzaby3lnO9pE/ZOWr46uRr6OucZ9frZNzhe/UF8aL1vEUEg1hnqDwcKp3WGNpLrFnm8/5eHXe+Ye1pHTlJlyNP6pO4CKZjYS9Bw8ejpVmW+Wl6Mp5JdL5hMcSmjqRpRq8NGTpiY3WpicT+VftCv1BSpXqk/JfAsleglkpqF0lm6v2Wj1jZdyieiFNPHKW2655brSfzfccINszznnnD322GPGjBmbbLKJomcQDdwuIhVXEZdYomyNKCfRxZtPPvlkKv177rnniSeeCEM1W73OOuuE+oxGZ1efFDWYP39+VJ9Rar3qnDlzMgZ+0PLLL59aSeqbFeQjjzyyoHGHv/jFL6L6lFIVlFo1f9NNN6ViTY8++kg4qswwOe644zKhBEEAAhCAAARqE4i+R9VbB0nF1MuRcrrKlDc6Aeoifu1rXyuTgmw+9rGPhZaXXXZZqMxrNA6Uw5a3KYQuu+yyBY071Mgqqh+6sgV3dOh5zico914vpUVtdtlll9SMQ9S+ibLzxtD/i2hSxykaOx9W/U+xNtxwwyWXXFKD7szNR+3q6quvLtMyu8IbHQFFlZlSVBoW9bkvy5RxGoL6XDWT1/dNQ4uijBCAAAQgAIHpJHDUUUdFC/7AAw9E9VIuXLgwDJo3b16olCY6fD733HOjxlI+/fTT0aDovIpZ9tkzVCY1YFlmmWUstxkhVRA9stSYLhOxEDR37tzoU86C2fgeLr300rUzX746dIquRr469eOPP672EBZTq8TKP/ZVdD1ZTi0rCRPviaZD7HkCox7o3XnnndEMaEGPLupoUFSZMi6sNkstW/zMZz4TTTZURpuozL7whS+ExpOh6XY+Vqs4KvUFYp5qVCeeeOIQa6S31+wQyzjapMZrEVyN3GZWSo6CrE6Xz2R07aTLST5iGBpdvpZZhpnyzyp9DsPPRopt9AWRIRZcPWJYd5mCq4ChvTSZJa5+MUM56txU3UwyTDajiS6TVxH6tldZtI1JmSlaGBR9zJmpX6UQjSI+lRZ0hzlBAwEIQAACwyKQcgOqbtxSMj+p00V7k2jnpU6kHQdJJUrlVnkoWV6ZRb0jpVDJP4n6G1FolrHoIolKJ3VJ1ejKo9zyuY1GESX9WYlKClHg+bOXTDljVrWCUlRV3oHuWdTPz1+wfWsMr1dshZqN1qkSqdSeq9bRBI9THP9R/OpCU9vOXCkW1CHeaM8y8LqznDshegmn7jPRM4p/t31ZKrcqYPRiyd9kCnzssF5SUbwiVmYIGe1NUoXtZ9W4a9MYlhSi98lUwUumiRkEIAABCEAAAhAoQyC1v4j8k1R0eSnO5/F/5chF7aPjWUWMGktZWPRgp8hvb9Znz1DvYaYKG+pTBSm/t5almSIvpGU8c0unqhCd9LB6HJaQf34XHVa4U1eqjg5HvsKux6xRXDWqL7Wjh9JPpVZvMBhtLVWT6ha7ipBpP9ECZpRVB3qpd5tTN9jMqaNvgIcTcalmVnI7ruhO8GpXmYy1E5S6EaUafDRX0btoyDAa11fWmCRJ5b/Sx0ktD6kdheQAmI0vRC+BzARF59esn/kxlcvujBXtFVCGBG6++eZQWUYT7bHyEfUad2igJbfRK18N9IILLgjt5f/NnDkz1JfRnHLKKVGzRx55JKqPKmsUXPsrhEmlCi7L7373u6G9NJ/97Gej+oHKM888M7Q566yzQuW0aaLv6Kjh6X2F8ii0KUt4P03tcVo+WSwhAAEIQGB6CKTeBVxxxRULEHrrICmf0SF9If92+PDDD5vsC5/61Kf8w6HLQq2pBPmT9qfDQw45ZOgnGmmCLbijI81/KnH5VNqpKxoq9yz1EljUvoyyh42h/xcR45QyTcu30UyixgUlN3HsEG/Lw6I+92V+9U2h3OeqmdS+bwqbGUWGAAQgAAEITAOB1VdfPVrMb33rW1H9okWLopvTrL322lH75ZZbLrqVQNSxVwrf+MY3oulsuummUb2UffYMlb199tknlfOCPlOQklud+QmKfGolgW82bXL56hCZDke+Ont0JyGtaCn/YS6r3M0339zk/gvdYs/wGfVATysQolvLazVP1a+iqRS77rprWJbLL7+8oExtMZXqAgrRo3uSpZaaFeL2/7Bv87FaarbyyivX4HbaaadFY33ve9+L6qsqe3vNVi1Ih/Y1lxN1mOM+n1qrR0tOcIel2HvvvUPlEDWprYBPOOGE2mfZYYcdonF//OMfl9/b8KCDDoomMkRldPWb3Jray+lSC7Gfe+45OcFDzPnYJSUPIOpPrLLKKlozXvVjWGNXfDIMAQhAAAI9IZDq4sMPhffWQRLJPffcszzPd7zjHVHj9dZbL6ofllKoL7300uapPf/8880TqZ1CC+5o7bw1jLj11ltrfvaaa64J09EXBjUXHOpra3rYGPp/ETFOqdreUjOJ0XQ6xNvysKjPfVm0aqZH2eeqmeC+b3oaGCWFAAQgAAEITBUBPZQJHz2EGsfk1ltvDeEohczzO328Mlx58P3vfz/6qOuiiy4K09eiitR8lIz77BlecsklYXFSmnvvvTcadNJJJ0X1A5XCvs022ww0mx6DStUhLB2OfFPbSdR+x1UvkkWXqfWw9jvEnqcx6oHegw8+GM1AajuxqLEpSz411iolLfx94oknLKITPvGJTwws7/3331+IZXGj+rFT9m0+NrU4bCDYzTbbLGrzk5/8pNJC22giUvb2mk1luId69iobTqXIH9VTmRqLr+30q666qsmjEH7wgx9Ek33zm98c1ZdR6s2D6Asc0U3RognKz66xDGuttdaKppZSRh+kvec970nZD9Sn8pzah3lgghNjkPlQ+gorrKAljHpJaGIKS0EgAAEIQKC3BLTOO5q3Aw88sKDvp4PkMhnurFbIvH8Y3cJWfloT79RPf6SyvOgab7MNK0vtuKPDym2NdMI3/CyRk08+2eSeCMNtDP2/iBinVG14elml/GtRHeJteVjU576sahVPmH1vq2bi+74Ja0gUBwIQgAAEIAABEdA2AVEO0e3EvvjFL4bG+XdUoulHH0LrGUe4uEGnmzdvXnhS0/TWM1QO3/ve91o+Bwrf+c53ojbyMKP6gcqtttpqoM2YGrz44os1cl6pOpR+hyPfG2+8MVrA2o1h//33jybYQ2WH2DM0Whjopb7/u9tuu2UylgnSTGDhL/rpqlNPPTVMRLfiaBfgW0YjyqDDuWg/ez2RVQXDAlJ7lbCWfUc3kCu5I91Akv28Zgdmu1cGrCprWh26R+tjzw03aVAio37md9VVV4VF1fWZeTkjtA81UUf8Rz/6UWgZ1VR60mMpVMpz6rnyO9/5TkuwhhD9WnD0I741Eh/fKDvvvHMm83rJYNasWXq557rrrkvVSyY6QRCAAAQgAIGGBGbPnl1IoZ8OkjI5FOdw/fXXL5S3P4carGoeVh/uvOGGG+QGRydk28ltC+5oOwVJnUXvgVx44YXR0GOPPXbg5Es04nCVI2oM/b+IUv4w45R8A9OYYqeddsrbKLRbvC0Pi3rblw2spok36G3VTHzfN/FNiwJCAAIQgAAEppBAysf+7//+75BGdOOD7bbbLrQ0zcYbb2yyCXoIrRGrHTrhzjvvLGjcYf57RL31DJX5pZZaKlqiqDI6w9DkK5baaCe6e0X07KNWap+wYf0J1EYbbVQjw5Wqo9uR78033xwWMPrwNDSLauoRiyY1UmW32DNFa2Ggl1oAsNpqq2Uy1jwotVvVwCVH4SaUysyCBQuaZ2ncUxjRfGyNTyEbyeh0n75nHXbEFqWk0NtrtmT+e2LGFzBrVoScpCOOOEIftii5PWP+NPVu9Pk0C6FR33rNNdcsmFU9XGONNapG8e3DD1H5oUORU/uH6dWQXXbZ5eWXX65xltQ+xupNt99++xoJTkwUkVFnrCeUmRJpV2rbmHq/1/80IBzKdZQ5KUEQgAAEIDBVBF566aWS5e2ng6TMD8U5bMHRGshZoz69mvnII4/oPbO77rpLzpKY33LLLQMjtmbQB0qjLqxmXo477jgNwsMT6Yv2P/3pT0P9KDQtN4b+X0STPU6p+lTA2Udbadga9WhHb6rk3+zqFm/Lw6Le9mVh3U2bprdVMw1937Q1NsoLAQhAAAIQmHgCqScI3/jGN+bOnesXP/pVPo04Up+gcXH1wptWw4TzFfpyZWEHl9QihvXWW8/PRkHurWcoMpX2vNAuG4Wi6VBfcwqV5TVbbLFFdBeZ8ikMxVINIPrB06EkXjKRqtXR7cj3/PPPD8v14Q9/OFSW1Cy55JIlLbs16xZ7puwtDPSitzJdO5W2g8kUIRWkG3j0Fq03DzMfwUx9/vLwww9PnWjy9G3Ox+oV3yYA3/rWt0ajqwgNG1hvr9loeXurnNJVZeqYdSt55ZVXqlaMujRttlTJxypzihZu9KE3rIy9733vK5O9qjZDeYRT9aQpe335Phq07777RvUoGxKYP3/+GWecUfKBkN4ZsteGhrtSs2EpiA4BCEAAAmNNILUb/7ve9a5CuXrrILXgHBZQDPFQO5Cdc845mlqK4h3iiUiqPIGbbrpJnw4M7VVHV155ZWEKPjSrremwMfT/Iprsccqjjz5ab9SsqaJnnnlGcD73uc9l7iF6TUUjjsx0Ved42xwWRUFNw2C/9q2ptYhUTWuoOREEIAABCEAAAtNAQK+o29MEK+9FF1105pln2qGEiy++2D90cvSrOwWzo4466uCDDy4oFy5cKN/eV5577rn+oZO13CE/AuqtZ1hpn/tXX301utl8OOEWIspo9LSuD6vK+jCNUKk6hLTzkW9YrauvvnqoLKnRwpHoyqGS0Vsz6yH21soevZU1WUpYPuf6JLE2iynYa2pI21ClFg1HP3+pqaTUMuVC4uN72NV87GabbdZPaNN8zQ6xRqb0C5jqmHWL0V2j6p9i5V3DenWz11571YtYMpZcvailih/VN1Tee++9JVPIv71RMpG8WWpXw3wsQpsQuPvuu7Vws2oKGjZo8LDCCiusvPLKGgrW20au6kmxhwAEIACBiSSgniharsJe3L11kJT5UTuHUT7NlfrItfpx7WWrjUujswzNTzH0FFpwR4ee5xoJql6OP/74aETNyKSuhah9SWXnjaH/FxHjlGhb0iSyRql6QVy76KVeJXQRP/3pT0dTcMo+4G1nWJS6fjsf7GdqZ0qC+lw1U9L3TUlLo5gQgAAEIACB6SEwb968sLBa5LRo0SJf/6Uvfck/dPL+++8fKguaPffcs6DRoV6h95Xy8aJv1GtFmm9WkPvsGVZaSpXaLaYw4VYoPoflCVSqDiXb4chXr4RFp/7CdT/liz8ulh1izyMa9UAvdSvL52pYoe9+97ujSemBclQvZfTzl5///OdT9hOg73Y+Vp/4a8Jw6aWXbhI9E7e312wmzz0MmtJVZVU75lHX3Lh8KzrkoJ1pQ6X8+JJdCxuGhfQmQKOhhXaZPv300+uVRe1HDUMfjz/ssMOefvrpeokQCwIQgAAEpplAdMQoIOuuu247WBo6SMrk2DmHegVK65b0OcXoS6vtYK93lulxR0844YQUogMOOCAVVEPfk8YwdhdRDdQTH0WvOD/11FOpYmqXAs1ip0L7oB/3YVHzvqwPtTCReWheNdPT901kA6BQEIAABCAAgaklkPo21p133mlM9GQqutJl4403NpuUoGmNMEhryPxVaw8++GBoI81uu+0W1Y9a2dwzrJTDng/BKpUF44YEUo3hpZdeapgy0WsTGPVAL7WutHaGK0XUpj/Rj+3qI5jRdFKfv4wuII6mMF7KPszHPvvss02gsUC5Cb0W4k7pqrIWyFY6RcPLbOC5Ujf6F198cWDcvEH0G+oDN/u1NEddcJ3ojW98o50OoU0CenNIH5mtvbZMWT3rrLO0dZnWlrWZbc4FAQhAAALjTiDz6snMmf/n4++9dZBUBS34SEOsaL25q/3JmqwnW7BgwRDzUymp8UJdqWih8R133BEqpdHqnOjL1lHjvLI/jaH/Ncs4Jd+WXKg23LriiitSlpnxbH/wjnpY1Oe+LFVxU6Lvc9X0/w45JY2EYkIAAhCAAAQgUImAPiUU/UbKt771LUvnrrvuMtmEHXfcseQ3iPSRTYtlwvXXX29yaniS/+pfnz1DK1oZQRijVXDfffeViY7N0Al0OPJNtepHHnlk6MXsW4IdYs+jmPiB3kknnRQScB/BDPXRz19qAcMyyywTGo+7pifzsbNnz25CMjPL1yRZxe3tNduwXC1HZ1VZy8D7dboR7SXYq63gfvvb37YG/Xe/+11r5xqLE2mMoYcoemVBqw+jS8jLlEJry3baaacylthAAAIQgAAEROCHP/xhlMOhhx4a1YfKaXCQwlLX1mj99xFHHFE1umYhNVd7ySWXPPbYY3IV5s+fXzUF7GsQmDNnTnSKXEltuOGGNRIsRKExFIDkDxmn5PlY6Ny5c00uCKl9AmTWK7ydDIumpy/Tw8VCw+j54fRUTc8rguxBAAIQgAAEIDCOBD72sY+F2T733HNNqacJJptQftbik5/8pMUywf8I5tlnn216E2o/+xg7z1ATONFXClv7OIAxR3AEejXydVka092GKo0re4i92yuitYfjm222WbSkl112WaiPfszktNNOCy3HXdOf+di77767CczMLF+TZBWXa7YhQBf9/2zYMJQUSWSqCEQ72hVXXLH/ELSHljYHfuGFF4aY1U033XSIqU1SUptvvvmll16qEunLmHqb57zzzov28akiX3PNNRq5aYFaygA9BCAAAQhAwAj87d/+rcm+8JGPfMQ/HKk8vg5SVSzXXXdddMbWT2eDDTZ473vf+453vEMj//XXX18bkYbvhKW2zffTQR4Kga9//evamSyaVEN3i8YQpVpDyTglhHb00UdHZwNDy4GabvGO17BoXPqyyy+/fGC9T5jBuFTNhGGnOBCAAAQgAAEI9IHAgQceGA4NtFGNPnzpdiP76le/GuZz1113DZVRzVZbbRXqNYheuHCh0+tRRWhQ/j3GMG5DTU88w6WWWqpJQaKlaJIgcbsd+Y4j/6GMKycee2r+dqONNmqn0rVDnu634Vz0cccdt88++/h5SH3+crvttvPNJkDu1Xystg495ZRTalNt2JXUOO/EX7M1mGSisKosA2dyglI3+ubXp7+3sPHq1V5llquCwBKlApB2DldeeWV17a53r7TCTK8T6T2k8CF0O9nmLBCAAAQgMC4ENI0YneBT/rfZZptCKXCQCkBqHO6www6pWNqN7Gtf+5qmbgsfHk3Zo2+HgObZr7322mjFyd3S4kt9cLBeTqJpuqRoDJWQMk4Jcb3vfe8LHx3JrMZ4tid4hzgsoi+77bbbwjbTBw1V04daIA8QgAAEIAABCEwYgdSL/drgZJ111nnuueeiO2mVH+dqBkOvxmmZWoGbnmXIh9dvQe8Od95556jelBPvGWorgYMOOsjKW1WIPmesmgj2PoEWRr6pVv2Nb3yj9hcJlOYtt9ziF6Q1eSjjyhawtwaktyf667/+63BVmW7aixYt8p8g/8u//EtYBO0r6dYfh0Hjq+nVfOwodh1Spzy6WuOardTy+QJmJVzjaqzrLfqx89Q34MuX8+c//3lorPdFQmVXmtS3cjXA6CpLnNcRcI9StIeZPMX77rvv+OOPz5NJ7auRj0UoBCAAAQhMFYH/+q//ipZXww9/YOlsptlBilKqqrzyyitTUfRpS0227rHHHiwpSyHqUL/99ttvueWW0QxkZiKi9qakMRiK8gLjlPKsUo+OfvCDH6QSGSO8DYdF9GW9fauNqkldnughAAEIQAACEIBAbQJu1VcY3T3quuqqq8KgqhuJRZ8xX3zxxUpZW8KE6UszcNXaxHiGqYVEDR+3RZ8zRlGjLBDocOSrVp2aXCpksvzhK6+8Ut54uJaVxpUdYh9uqaumlrqVNfzuYaVsaAFx1N7dpS3oxBNPNNmE6PuKFjqOQt/mYxuuCo2uWhnKSrWpvWaH26pZVTZcnv1NTd8bCjP38MMPh8ryGq38DV/aUPS5c+eWT2TUlm9961ujp3jooYeiepSdEJAfcMIJJ2gCt1tSAAAgAElEQVRMcv3116cy8KUvfSkVhB4CEIAABCDgCOy9995RFJ///Oej+ql1kKI0qipTHxvVkjKtJ6uaGvZtEvjhD38YPZ0G/5n5iGgUp6QxZOCkghinpMiU1997770p4zHFW29YNM19mfYojU5KpBpGy/pprpqWUXM6CEAAAhCAAASmh0B0U4Ozzz5bBMINbKT8+Mc/XgnO/vvvH9q7ZxPR997322+/0D7UTIZnmFpTklpvF3IINXoq1GeXPsxwrzTdjnyjK7HOP//82oi0/UTtuE0iVh1Xdou9SUmbx43eyppsN3jDDTfoBuL/3Xrrrfl8LliwIDQ46aSTTJn6/OWcOXPMZjKEHs7HNllkHF1h/OEPf7h5ZU3zNducnqXAqjJDMeHCX/7lX4Yl9G+yYehATWoB0CabbDIwbmsGa665ZvRcA7ulaCyUoyaw9dZbv/TSS9GzNFzjHE0TJQQgAAEITBKBiy66KPqlA5Xxgx/8YLSkU+sgRWlUUmrWL/qxUb2nWGNJ2SOPPFLp7Bg3JLDccsu5OfcwnV122SX18nFo7DQ0hhSZvJ5xSp6PH5oaIPg2BXnc8VYaFk1MX3bZZZcV6nHgoT51NNCmQ4OJqZoOGXJqCEAAAhCAAAQgUCAQXfWlCQqtC4n6k6mdjwvJ2uHaa69tsgl6NvHyyy9/97vfNY0J0b3NLNSEifEMtf25FcoErbcTfzusJDzwwAOV7DH2CXQ78t1rr738zDi5yYO873//+2GCVTXR+0A+karjym6x58sy6tDoEh8tDK13B9DmNdtss42+nOD/bbHFFvlSHH744aGBGp5Sc/ro5y+PPvroMNZYa/o5H1v7Y7IqTnQzuXe9613Nq2mar9nm9CwFVpUZigkXovuH6clrk+3KUtt+rLbaav2hmfru0uc+97kmmdxqq602DP6qPoFrkoEexj3mmGMKSPQxlxpMVGW//vWvowWs55dEk0IJAQhAAAITRkAvIe27777RQuljByl/YGodpCiooSi/8pWv1Ejnf//3f2vEIkoTAgcddNBKK60UTWH+/PmpjcGj9ikljSFFxulT9yXGKSG3ddddN1TmNR3ibX9YNDF9WfRN93xFFz5ykTduP3RiqqZ9dJwRAhCAAAQgAAEIpAhsvPHG0aBvfvOboV5vvqWGBqGx08yYMWP33XcPQ//t3/4t+ipjageUQgoT4xlGFxKpsL/4xS8KRS55GF3/UTIuZqnm3c7EwrbbbhutgtrPnYfywaIWxpXdYo8yb02ZWuJz00031chDdPOa6B3YT1wfHd5ggw18jZMvv/xyJ0Q/f3nccceFUSZS0+18bO1beupzBG95y1uaV9M0X7PN6VkKrCozFBMupL5vnVoZNhBH6s0P7fcrt3tg9DYNolsQa9ly7W0Y77zzTkXX4uvCX98K3iZknUvdfwGIRlnPP/98jWxE91CtkQ5RIAABCEBgSgjoVaTMmoNTTz01xWGaHaQUk5L61Luk9fas/fSnP13yvJgNkYB82mhqejNMb3am1pyFUWgMIZOSGsYpJUEtueSSJS19s67wtj8smua+7DOf+Yxf6X2Tp7lq+lYX5AcCEIAABCAAgYkhoI8wRr2s6KuGes+wRsGPPPLIMFY0fQ2ctRd4aBxqonmW2dg9odttt93C0klT9Uujlkh0/YeFIgwk0NXIVxlLLTA6/fTTB2Y7NNAT2yb7nIUJltfUGFd2iL18uUZhufnmm0eTrbcTWJR8aumqf97ondMtpox+/lL36tVXX91PYQLkfs7H6lPU9TaIiT4d0PLBkp3swAqd2mt2IJnyBqwqK89qvC21dDf6ZEgPjWrsJiUW//Ef/xElcsIJJ0T1HSo/9alPRc8e/fRy1LKg3GeffQoaHUbvR6HZBGuiw6r/+Z//meAiUzQIQAACEOgDAX2DYNasWamcaEybGXtMs4OUIlZSn/ogXY3FH1oUWGN3+pL5xCxDQPMpmUmf6EvY0dRoDFEsZZSMU8pQqm3TFd72h0Xj2JdFN0TUI4RKsxPXXXdd+TtV7YbUJOI4Vk2T8hIXAhCAAAQgAAEItEOg/Fqxvffeu0aWtttuu5KxPvaxj5W0nBjPMLUyQx8hrbGPg1z6kgAxSxHoauSr/KQag1YK1lhW8olPfCJVxpS+w3Flh9hTNNrRL7PMMtE1sroD2AcoS+bk6aefVqzQeJdddgmVBc2ee+5Z0OhQUwpqeAsXLgyDMu+ch8bjountfGz0Q5Z5qnq+oy8phzbR5YOhWRnN1F6zZeCUtGFVWUlQk2B25plnRotR41UATfW+//3vj6Y2Z86cqL5DZWrptAqu+1TVjLmNysJYn/zkJ0PlVGmiDlx0cfFALNEdzrQkWe8hDYyLAQQgAAEITBWBxx9/fKmllsoUeeAi8ql1kDLQygStuOKKUbOHHnooqs8o//zP/zwTStBICZx88snN06cx1GbIOKU2ujIRu8LbybBo7Pqy1HeCfvWrX5WpXGezww47lDfuynLsqqYrUJwXAhCAAAQgAAEIlCcQXU8QjZ5a9RI1NqXeTozu0WAGJhx44IEmDxQmxjNM7UQVfcEmj2UsXPp8EToP7Wrk6wqeWuL5N3/zN5XIaIJXWxxViiLjDseV3WKvCmq49qkPLB588MGVTpT60uXaa689MB0tbttxxx1DM22Ic8YZZ4T6idwXprfzsccee2zVJYYf/ehHw1qT5oMf/GBUX0M5zddsDVzRKCzRiGKZTOWHPvShaMG0uEofLowGpZSHH354NCjlTUaN21SeffbZ0dO95z3viepTSt0HU991etvb3paKNSX6t7/97WFJtfWI1puH+rwm2pA222yzfKwwdMq/SRoCQQMBCEBgwgicc845q6yySqZQ11577cAVydPsIGXQDQxKTbCqUgbG9Q3233//rva397MxtbKcpZ/97GcNi09jaAKQcUoZeqn9q6JzhX6CneDtZFg0MX3ZX/zFX/g1mJEPO+ywTGjtoKEPISemamojJSIEIAABCEAAAhAYOoEyaw500ibLCKIfwQwLsummm4bKlGZiPMPUDm16GHTllVemih/q99hjj1CJpgaBTka+Lp//8A//EM2wnjs//PDD0aCoMrU+LGo8UNnOuLJD7AMJjNRg6623jqavvaZuuOGGaFCo1L0iulGZ7tslR+V/93d/FyarDXHCBQ/aXE2r0ELjcdf0eT620jvkagzRjcrUGGbOnDnEapraa3ZYDFlVNiySY5COnqoef/zx0YxuuOGGWgkeDQqVurxTa8ZTq83CRFrWpFa5qtM65phjSmZGq6NSH9i65JJLSvZzJc9V0uzFF18sadmCWeq9n/XWWy/1HCiaK/maWsgcBpUcyPkRH3nkEf8QGQIQgAAE+kZg9uzZVbOkfUa1b6i+uK2eN/8KlN6W23777QemP80O0kA4GYOU56Npo/vvvz8T0Q/SBGJ00OjbII+agF7VSr0dWPLUNIaSoKJmkzpOiRa2tlI3au1bHEZPvZdplp3g7WRYNHZ9WepBoOZ/By4WVP1qn8XUpITVfj1h6EPIsauaetyIBQEIQAACEIAABNokoEFodJeaQh7mzZtX0JQ/1CtwA401SKn0wHtiPEOtz0jtUKVP12nWbiA6GejBnFahlbHEZiCBTka+Llcrr7xy9HuICl1jjTVKfi1KrwyFK4EGlloG3Y4rO8ReBs5IbS688MJo+ttss02Z1YS33npr6jOX5T+qUP5Txaeddlo0t+Ou7PN8rN4hL/kqoLqMVGOIbkDTpNam+Zptws3isqrMUEyF8NnPfjZVTm31UWYRsfafyFzecotT6XerV8ZSnZy+76snmgM/8q21dCussEK0FBo8dPVShTZOU7cxxL+LLrooWsaSyug63yeeeEL8n3vuuTKJqP+Qrxm1fPe73x3VS5latbbtttumglJJoYcABCAAgTYJHHHEEepDdyr3p0Xw6vL0vUt1f1q6lM+nZjRSXxYII06tgxSiqKRJzSGuu+66+ckg9c6XXnqpanPgBKJsKmUJ43oEvv3tb9eLaLFoDIaiqjCp45SqHAbaRxeQPfnkk/mIXeHtZFg0Xn2Zbu+p9azyDTSPnBrHacCo5xbR15DyjaEQmkp/FEPI8aqaAigOIQABCEAAAhCAQD8JyGkcmDGtbxhokzLYeOONU0Gmr7FqbWI8wy9/+cvGoSBo1k5zPgWlf6iHcZoM1IM5X4nchEBXI1+X5yuuuCKVec3i5p87a7sTzffWfmWo23Flt9hTzNvR77PPPqkT6QnvddddlwqVXuP9LbbYImqgWYJ11lknGhQqxb/khpTvfOc7w+iToenzfKyua83e5NcZa81J6gNxqlxFH241TfM1OxySmkqb7L/oKmktA+qq1Pfdd19YcwsWLKiRn2hSSvyVV17JpJZaXOVyJTLyAF544YVCCs8++2x0ZtzKoo0WC1EKh9Hc1it4agVYvuDKj0pnGQ6Fo48+WplU4n7On3rqKRFLbSPpEnnsscf8KKOQh74gNyy+0xRqJFprmcsnVTUucW2V99BDD0X5qO70kbJMBan5RSOaMhNXjohOrfqVoJZsURAgAAEIQKA1AtEOJdUZNddnuqpUkbtykJSfKJxCj5zKtumjiVTlEPU3MomoW89UlsZ+BR9J3f2vf/1r9ciZWIUg9eDyLvQX9uA1ihyNUhW1Y64sFbLqDge6o1ZlNYSqFVTpFNp5N1oiX5lh1cPGkMltlEy0eWTafzSR2nWkE/moC/I4jlOGfi1Ex/UCVeZE7eNN3SJczY5uWNS3vix/BQ38/K6mR6+//npdm/qTsbjlB+b+hVPmDpBpGPkhpPLjn8vJ+cL2rWrK8AnvcqmGXeYyDFNDAwEIQAACEIAABJoQ0IRD6JL5mrx7VubUqTGInUVTHGXSKdj0zTOsDSr/xFCUZKBqkg9pBPS4TY6oAcwLI3Uyo5VbG4UVsJJQY1iRT1/5zyAd6cRCammLy4/GcZp08mf2XnrpJQ3xyuw46FLINIY+jyuV+dFhj7afNgd6ugFm2psq3d0BXN3pPqAGkL/7KbVwcUK+zd9xxx2ZPLggNc58Ih2GRm9EynamwRdy2+18bCr/hUpxjUFZ1YWvZqBaVuPRDE/BrHA4EEL0EihzG+/wVlmovrE7/P0eP5P9F23TZVrViLBEW3m9G300qTK3m5Krd4VOl3oUYOHa1uHAe300t/UKXnsqUzesMOehRqVWC9FvGBRq5AyNqKn4yUYfUIWZaa4p1Ei01vKXT5lHkmpU6sh1Lv1JyN/BXaF8GlG5ZKuuN9KLnhElBCAAAQiUJxDtUJp3W9EU9CS4fMZ8y5JdyXAdJGUgCkddpJ+3gXI0kXyXHaYZ9TfyiYh2tBZ8pVLQn68JZed9hXpfU+jEaxQ5GqUqasettjsaYi+vqVFB5ROX5cBqyrPqW2PI5zYkE20eYhJaZjS162jyxikDZ38yGKNBqWGp5hCj9r6yE7w9Hxa105cNvIIG3nb8XiAl62IPB+9l7gAl+/1C76OmVe92UfJ07VRNGT7+ReTkTvq+MBtoIAABCEAAAhCAgCOQ8g+dvp7D47MduACi9qinV57hQKfdZ1KQSxZE7nrosfvVF11dVBtvIZPRw+gAswmK6FnyynrDikyanYx8LT/5Kvaru4acbwzdjiu7wh5tP/Xue7UHegNvkpXqWlveWHMqLww8hUCVT61ly+iNSCXKN/hCJjucj03lf2ClDDQoU2vRS6DMbbyra7ZQceN4OHNgzWEweQQWLlz49NNPD/zkkL56q7Lr84UDCWiFqb6kPtCscwN95F43C+25ms+Jilym1EpEz426+vZlvggdhgqIOhLXeFLZUGjeoBBx4ItHsj/hhBMuuOCCQkQOIQABCEBg2gjoKX7trnlqHaQmjURfThzoWeW/hunO/vDDD999992pLa+b5JC45Qno/c7UB9/LJEJjKEMpZcM4JUXG9G95y1uiI4hDDjnk6quvNrOo0AneroZF49WXNbztqLr1HGv+/Pn1PojZ8hByvKomeimhhAAEIAABCEAAAr0ioEfpmYdc+++/f8Pc7rLLLpkU9BBE39LKGGSCJsYzLFmQ/OM27Vhz5JFHrrLKKhliBJUh0MnI1zKmp3gbbbRRmTlAixIK+Ys6tHeabseV3WJPMWlHr+9g3nvvvfXG44Uc6j4wd+7cgrLMoXaDy3xOV4sdy39Ss8zpemjTq/lYbVD33HPPlflEdYakvmw20lqb5ms2g71MUE2np0zS2PSZgL5rXvI1goGl0JKy1VdffaBZTwx0s9Ai36EsXde66Xnz5vWkXL3Kxs033zzE9xL0aniZbyfPmTOnVxDIDAQgAAEItExAg0918bWXlLncTq2DVLuy5Fmpp64dXRHllanilI7mnpqkQ9zmBJZffvnoVlslU6YxlASVMmOckiLj9Ntvv33U4JprrjnnnHOiQb6yE7ydDItU6jHqy3Tb0Tcu/ZqqJGtOQ8+xXn755UqxzLj9IeQYVY1RQoAABCAAAQhAAAK9JZD/6N7aa6/dMOf5pxJHHXVUk/QnxjNUQbSqozYKrSLSyx5ahVA7BSL6BDoZ+boMzJgxQzOE+avSz2ooa8u6Mnt+hxG7HVcqPx1iD2m0rNFbXgM/hjswS9rzTPeBgWZRg+OOOy6qd8pTTz01EzoZQWp+/Zmc13vjWjjRZP2JlpSlJgCHWF/TfM02wciqsib0xjuupl8b7k6ptzG09dcYLSlzFaY3SHSH1bPn2vWnJVP6AHy9ddO1TzpGEeU+Pv7442V23cwXSpz1ieXySwDLbGmWPyOhEIAABCAwdgTUTcif0bIkDT5rvyTql3pqHSQfQiVZVSC/qFIUM9a8g7wyV3EazjVZ0mRpIjQh0PCVCRpDE/iKyzglA3CLLbZIhR588MFlFpa1j7erYZFAjVFftvXWW9dbWKapRhVThY1+9SDVWgr69oeQY1Q1BVYcQgACEIAABCAAgb4R2HnnnVNZ0rMJeeOp0PL6zBKZ3XbbrXw6UcuJ8QxPOeWUeouB9JBOi9KicFDWJtD+yNfP6plnnqmRmq8pKetaG7gNeSapbseVyli32DNkWgg66KCD7rjjjnon0jIDPQXWnmf1oiuWlihkdjk54IADaqc8RhH7Nh+r3q3eGgw9JmhhSZmr2Wm+Zmu37clfVbbiiiuGdKLK0Kw1zezZs2uca7nllovGKu8u6079wgsv1Fg0qjuUZn5/+tOf6vlfNA8llfUKnkq8fMGVgp49awY5+r32VPpOry3KtGRKi9/zZsMNXW211YabYCq1MmsE11tvvVR0X6/xQL3HAy4RLREQ51Qj909ksl4e0lNtNU7TIEAAAhCAQE8IVLqf5/OsgaIGnHoPUpMUGnZqsCF/RsOAfKxKoZ07SMrtUHykkl22wYn6G2USkV/02muvLViwwJIaKGgBmdYCat7Bt9SSpswbri+++KJvHJXL5LYQcSioLc1K7qjFKilEK2jo45rM+owyrPrTGMrkdiD5qi0qWkdVExmvccoQb+/56nj729+eNygZ2j7e9odFDkXLfVm0JZRs/HoAoA4988SuULmanVQPYlONa6yxRsFAh2UGtjIb4hCyZGF10parJoQjzVDukJbySPs+OwsCBCAAAQhAAAIQKBDQ8DO1mEBfVCwY1zv8+Mc/nopY0uFMRXf6lj3DJk57viD6dIBces3X5c0sVA9xNKGn0ZlpQmGkTmZ0LiWqDDM2LM3oqkM5bH/ka1g0UtN4LTO/Z5ZOUGPQmiQtRyvoqx52OK60rHaI3eWhq4GeNgJ/9dVXK70trNuFbgJaZhC9EAxpGeHv/u7vomZaXtxwDUM02SEqU/ecGne/TuZjw/xbC9S1oPot/7xejUcPF8rbqxaiLaf8zIyrx86v2SE2pzaSUiXxBwFtOaaXCQYusdJdXltKaOHOJBGTs6tC5f1d3cg0c6074CQVvLWyiJvolekMNAjUswR9BL1h3rRWUgvaVK16vK0/CWreyobaecOUiQ4BCEAAAlNFYJodpHoVLWLqeVNjGNfRa6oon7jW/SsR+aVyz/SnKQA5EnIPND2Rj0horwjQGJpXB+OU5gwzKbSPt/1hkSv+GPVlus/rbq/HDzZ4tMeEEvQ6nBaUj6gv6GQIOUZVk7mUCIIABCAAAQhAAAIQaE5gYjxDvaiW2cZCXr1meDTt05wYKZQk0P7I1zKmVq1ncza4K8wWSq/Zv0Jj0HCvYOYOtUzNkh0odDiutLx1iN3y0JWgm0DmibDme7WliPgMMXtKLdpsNAkzxLOMS1K67no1Oa/2kHqH0D0paL4kYChVM83XbHmAM2QavdhQTi0BfcJce0SpLzcCq6yyil7hja76NJsJEHQtPPPMM1oz53qgpZdeWotq9bfsssvWWBc8AUCGXgRH+NFHH1Uvoll7l/6sWbO0nHndddfVSuqeLxsfOhAShAAEIACBMSIwtQ5SvTpatGiRfCrtLiaHSimou5dPNdwt5epljFjtE6AxNGfOOKU5w0wK7ePtcFg0Xn2ZQGkk7n4zNTgZQeNVNZPBnFJAAAIQgAAEIACBfhKYAM9QPvwTTzzx8MMPi7CbF9KkkB4y8gCowybnxqGdPADVqTVDqD9XfDUJ/UWfusoyOnmoVWVRfRmeSrPDcWWH2MvAGbWNVvg5+BJUg7UrMZ/Pe+65Z8MNNwxtBD9UTo+mb/OxL7/88vPPP+/4L7nkknpYMKL20LCKp/yazdNjVVmeD6EQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCIyEgNZzRBeaNFlVNpKMkmifCOgLvJdddlkhR9oRTZ8YLig5hAAEmhBgVVkTesSFAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKAmAVaV1QQ3xdG0I5d2vQoB6EOQbI4YYkEDgSYElmgSmbgQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhBoh8CXv/zl8ET77bcfS8pCLGgg0JAAe5U1BEh0CEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAATqEGCvsjrUpjvOjBkzQgAPPfTQ6quvHurRQAACTQiwV1kTesSFAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEGiDwHXXXReeZoMNNmBJWYgFDQSaE2CvsuYMSQECEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgcoE2KusMrLpjrDyyis/8cQTBQbXX3/91ltvXVByCAEINCfAXmXNGZICBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgMAICRx22GHhkjKdjyVlI4RO0tNNYOZ0F5/SQwACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQj0iMBOO+2k3BxxxBHbbrvt7Nmzb7nllkMOOeSee+4Js3jFFVeESjQQgMBQCPAFzKFgJBEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABKoR4AuY1XhNjfVWW22llWRliqsmVMYMGwhAoAYBvoBZAxpRIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAS6JHD99dd3eXrODYFJJ8CqskmvYcoHAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEJovAlltuufXWW09WmSgNBPpFgFVl/aoPcgMBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgkCdw88035w0IhQAEGhJgVVlDgESHAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKAOgddee61ONOJMOoEnn3wyU8Tdd9/91VdfnTFjRsaGIAhAoDmBmc2TIAUIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBQlcASSyyxYMGCMJb0oRLN9BCYP3/+ySeffM899/hF3nHHHQ8++OB99tlnueWW8/XIEIDAiAjMYOXviMiSLAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAbQK2poWdyWozJCIEahNgVVltdESEAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAwgQTYNHICK5UiQQACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQqE2AVWW10RERAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIDABBJgVdkEVipFggAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgUJsAq8pqoyMiBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBCSTAqrIJrFSKBAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoDaBmbVjNon42muvRaOn9FFjlBCAQCUChetrxowZLnpKXylxjCEAgTwBu+JCs0xQaIwGAkMnUOgFLP2U3gwQIAABCEAAAhCAwDgSMCen4IebXoUqBI1jMckzBCAAAQhMOYFUX5bSTzmuaS6+7wIVOGSCCpYcQgACEIAABCBQkoDfvfqOWUpfMlnMJomA3zAK5coEFSyHe8heZcPlSWoQgAAEIAABCEAAAtrGLHMAACAASURBVBCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAATGmwCrysa7/sg9BCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACB4RLo5guYhTL4G/r5csGszGHD6GVOgQ0EekKg6g6HdnVIUNzCYb1CWSL1ohMLAmNEoOoVVyiau+6csmFShZQ5hMAQCfh3dV+uegrX4JukUPWM2EMAAhCAAAQgMIUE6vnV5qKYIHQuKV9TlWeTuFXPhT0EIAABCEwhgXq9XhTUEJOKpo9yfAkU/JnCYaVyNYlb6UQYQwACEIAABLolUNWzKnSRhcMmZRliUk2yQVwRUKtQdVRtG4bOj1s7EUttKMLilSVDSU6JlGyvUTMpo/pC3srYFKJwCIEJJlC4IqI3F9/GGbhfp7fQaFyhMwOHMWU2wZApGgQyBMpcEbJJmaX0/hnL2Pj2yBAQgcKtO8okZeP0qVA/qTI2vj0yBCAAAQhAAAIQGB0B80zy/rMzk41v5pQWlMpkyVOkoqOHAAQgAAEIDJeA35dZylKqw7IgE5xB4dBi+UIZG98euZ8EzG/JZy9q5pTRID+1gQa+MTIEIAABCEBgsgn43WLGm3JmzsDMpHR6h8j0BWJlbApROOwVgVTN+pmUTcospS9E9w+HIo92rzK/WfvZLej9Q19ORUnZ+PbIEJgeAoUrInU3MTMJvk3h0ILMXiRTNtMDmZJCIEPArhrZ+LIfxb+IUja+fSapghmHEChJwL+r+1FM71qpf+ib+bLZSOnLvg0yBCAAAQhAAAIQaJ+AeSZyuX3Zz4npfaVz0S3I99j9pBQlauMnhQwBCEAAAhBok0Chz3Kndr1VtM/y7f18pvS+DfIkEbDmYYJK58vhoRXfN/NlM0CAAAQgAAEITCeBVLfoO1q+jfT+oUEr6KM2MvaTtbgIPSdQqLXCocu8atz0EvzDTOksSsamdtBoV5XVzlaNiEYzdV3VSJMoEOgnAXf7UN5GencYWHZ3rVlmBtpjAIHxJeCuNetoxrcg5BwC7RBwFwsuWTu0OQsEIAABCEBgOgn0ykVndDydjZBSQwACEGiHgM2+ur6vnZNyFgg0IYBr1IQecSEAAQhAYFwI9MdJc09kxI2HMn1oPL2asBoWkO5XlalxW/s2ISyeBUmwS7RgZjYFPYcQmDAC1tSdYIfuJvXqq686oVBqM5OQsvGj+PZ2+/MNnGxmYRAaCEwMAWvnJljRdHWUuaBk5seNXqSWJgIEuiKgVmoN1YRCZkzvhELbNmMzMw0CBCAAAQhAAAIQGCIBczZMUOLmn8hFT53L7J0bY1FS9pasE5wnb4n4saJK3wAZAhCAAAQgUIOA9S8muERcl1RmVkoRnbEimlAjJ0SZBgJ+M/Nlv+ymt6ZlmqiZr0SGAAQgAAEITAwB6/4KQt5JM2NxkOwfpvw0s5GwxBJL2KFPMqr0DZBbI2B1YYKdWlXse+86DG1knNJbOu0L8YxWzUe0tErEZvFkkCq8gvRnZ3Sy+/WjhDYWxQTZ+GamR4DARBLQBRKWS5eA6f3LwV1NLsjXuxRM78wsWT81Z2NBTgiTKhhwCIGJIaBLIHoVqIC+3mQJ7gLxNY6GNKb0+UjpX3RyDf1QX45G9w2Qp5ZA6rYsvQWZEFIyz01BZibBNU5nb3rfppCUs/EtCwYcQgACEIAABCAAgeYE5KL4XoqfoPS+Y+PM3K/zbXxjyQpyoU7vbMzSvBrZFCL6h2bmK5EhAAEIQAACNQi4Xsn6HRMKSRX0dijBejFFMb2T/UNL0CktyAQzcEJKXzDjsEMCKYfE10v2Dy23Bb3ZSFDV+4cWRYLpC8qo3rdBhgAEIAABCIw7gZRrJL1NSqhDtKVgrj/1Y1l3aUqncZbiY4JjZWYhOksqDELTMgFXTdHK8pUmS7B6d1n1g0z2S+GiWJAEk30zySl9wSx/OOS9yjKNVUF+qMkpwfSuAHboBDv0iydlVO/bIENgYgjYLSBs9i7I10s2ewkuyAl2GCXjYvlJycwpJRT00RRQQmAyCKjZW8v3S+SUFmSCs9GhXX2hYDZ+giYXrq9CymaGAIEUAb8J+bLs7dAEl4gdOsEObfzjx3Wy2bgU7NePYkoECEAAAhCAAAQgMEQC8pD153sjTnZKCzLBTi0zKd2hE0zjpyADF9dSMMGP6ydrMgIEIAABCECgIQHXQ1ki/qHJrmPyD83ef3jpRuhm5jo7WZrGYkWFkmbRuCi7JWB1rWwU5MKh5dPpLdQEf57HlC5Z/9BPJ6o3AwQIQAACEIDABBDw3SS/4yvoFWShCnKyE5ylNCYIix1KdnrHyuld9EKQi+XM+O2cgGrNrzjLj1NakAnOQIdW9aFgNpZaQbCGIX0h5YJljcMhryqrmgMrmxP06wSXjskSHESn9/1X/4xm7yuRITCpBKzBm6CSSrYpAx3qYnF3DbNxgiklvPLKK7J0GmNVSMeimwECBKaNQOoq8C8WXUd20YmPDl0s/Up2h/pVkNNMG0PK23MC1lxdPn13ywVZ/u1QgskWigABCEAAAhCAAATaIeD8EN8bMY3zvXUowfdqTO8snXPucuubSWPJWprOzPSFMvpJFYI4hAAEIAABCDQnYB2Q68tcglLaoQR/VsrsnY3sC8bNs0QKE0bAbzOuaL535Idaq5OZ9BY0YUAoDgQgAAEIQKAMAesHTQhj+V2qQp2llO6Zvg6dI+cEF91Sk1B49B+mj6ZvBKz6Chnza7PgvZt/JRvJ7lC/SsFpCkm1fNjeqjKVVn+ueL7gWFixC0F26F9sprRYEkxpgh+KDIFJIuDuIyqR3UqsdGr/dgk42R3KUqvHLOKSSy5pUdzF5YJcgi7I2TvZ0rRYEixlk/1QZAhMEgF3afht3i+dXSAS7CqTgTs0S//6sigW6gTfphDEIQSGRUDNz2+BJkvwW2BB7x9aTqQ0va+MyqZEgAAEIAABCEAAAs0JOL/FeSPmwxQ8Ewu1OSVpLKIE/fkaP7oFSbDoyrZvY6XwEzElAgQgAAEIQKAJAXVALrrrkiQ7jXU6TmMdk9O7Q9d5WQoWV4LZO9lsTJCev2kgYC2hIPgtwQ+S3g4LrpHpjZtpTLAgBAhAAAIQgMBkEHA9pno6/ZkcLZoMpHe/srTn79JouZhF0aF6WJeUlBJcFNkUel6LYoIs83kwS4QWCPh1YRVq53XVqkNXa4VDM/Mjmo2FOsG3KQQN/XCxI1g7aRXDL4nJTrBDa+7S+CilNxsJoew0hSjR3L4e+w8L1woGlmxBzyEEek4gdTtQk7Yga95O6fT61cWlPyugVpKZpfTOTBrrsVwUs9Ghv/bZJe5Sc3ElO2M7tHNZkK9BhsBYEIi2Z+Vc+lSQu4hcqH9RvB5j8QygRZfgX3f+xeXr3UkdNIubz4kz5neaCfi3fdcaHQ3Jdmiy06h1WZDbvVJRnMb0lqw0rjWagdn42C3UV1pOQiUaCEAAAhCAAAQgkCHgO8O+me+WOL15Jn4UKW0ILDOLZYJT2qEERdGf9Pr1X8pySncuZ+/LfqjTu9+U3rdBhgAEIAABCIiAdS6uM/IPo3xk4Gzcr+txnOxPMf3eyFuj5mT3qyh26EfxM+OfWsbO3lci94qAzeEoV+aEuIq2Q9k42enNUocW3Qxc6ZzeKV0bMAMnFCBIGdXbuQr2HEIAAhCAAAT6TKCk/+P3fYqiQxfRPXlxGjfJ4IKs21XZ7RQSXE/t7PXrnDSL4lsaNGdsh77g58rXIzcnYHVRSMrpo6GuNl2QqxozNnsJvux76a4ZuNP5emn8KJaf11P6w6NqU9YQau5V5je+UDaNfyX4mZOB+5NSNs5MGmfjBDPwlZIt1OlNY4cm+Jbi5Q4tFAEC/Sfg2q3dAvwMW3uWEDZvF8W3kawLzRm/fs39frWZM9OvbjouyGnciWyttA5dqJ8B39KXnY2z168fBRkC/SegxuzarbVqE1KZl4GuKTMzQfaS3aH7dRr9Wjfv20jWn101EtyF6fT6tUR8s4LejBGmioA1G7/UTmlBEkwumFmQBNdT+JZOlt5i+aHO3oJSgkWRoJZshyl79BCAAAQgAAEIQMAImPNg/rBpZFPwK3SoP7N0ibhD6d2hCS6683NkU/DSzcwJ7ldm5hdJ1l/0FKZ3p9Chpebs+YUABCAAAQiEBFx/YZ2ICQVLX+9k0/h9mT28VHQZ+Da+mZ94qreyuDL2bXy9nw5y+wT8erGzO6UFmQ9jBk6QgfvToWx8M+mldL9mLMFeSnShoYEz9n/NRoJajh36NsgQgAAEIACBfhJwPVfU8/F7NCe738IDPiuXQt2f07ie16WvKC6WdZR2Rut5FdfZKLqFuqT8Q0tB9s7SCc6S32ERMM4G34TCKUwvQZXuH5qllE7vfqV3grUKp7FQHVq1SvD1FleC/sxMcsHs9fBSPzVXlZVK2zNyebUcC5bJntXvReldkAlOqRI6vQ5Dv9ZsnKDfqOD0/EJgjAj4Ld+yLaW7ClyoXRoykOxuLhLMzOltKsGuIIsowf4U3U4kSyXiopvSndSUdmgGTrCzFPQcQqD/BPxW7S4B96vLxAl+EaS0jQAValeNb+NkP65iOWUhevQUYVJoIFCbgN/2XCJ2u7Ygp3eH/q+d1Lf0Zd9ADd6CTJCBnc6MESAAAQhAAAIQgMBAAr5r4WTnXRfcb3kd7s+fZjX3Q0EulgkWpAy4iE6Qh++C7FwKdZk0wc+zKZ3gzmIG/llMiQABCEAAAhAICbh+x+mtNzGl06ivsSBZ+qHqcezQn+MtdE/u0KXj7F2ChZTD7KGZDAKuAagsTlCzKWgsyC+v+TPO2I/iyy6K01hrLCRoSfnpI0MAAhCAAAT6TMDv1CyfUrpOzVwpBblOsNDZWV8pAz2v16GLYkm5iIplfajTuPP6xn7Klo4E5/tZdCe4RPyzIA+dgKsjl6yrKfdrteOf0dWUVZNq069c39LX+7XpP5KOnsJPZIjyaFeV+SW0kjulBdnlIY1Thr8qsF0hr1v9wVJ6Z+yIpOSCmTPmFwJjR0At3G5DrrXr1wmuLAp1F5QEpzdBh+7G5DR+LHUzdhkqHT/IRXEaf1JeGqXjTurLPlI/HV+PDIGeE7CrRvk02dqzCVYK2bgrRRp3OYQ2flKSZW9Te+4U+nUJRuO6IH4h0ISAmpZrXe7Xb3JO4xJX45TgW0r2Dy3UCS7UGbgU7NdXpmQzRoAABCAAAQhAAAIZAs5ndgZOdk6INCa4UHcoG3k1zgNx9vp1xs7Md+CdmfTOxhloktdFNHsnmFKxJOuvoHeHhV87RUHPIQQgAAEIQCBDwPU1MrB+xATXAblfM8skVUjE+i8/iiXulFEb3x557AhYFZtgRfA1Ts5ofAOTTbA0JYSJuFBf79sjQwACEIAABMaLgHo016kVfp0f5c882EN2FfD1SP/nib/spdSvE2QjwY/i5ihcXNM7Y/06aO7hjpNdfpzsYpmMMFwCVmVK1mTjb4JVkwS/YViUQq58vexTlW7pu7MXEhnu4XBWlVmOJRiUQkYVpD8pnWCyM/vlL3/54IMPhs3dmRWScomUVDqzVDphImgg0CsC/gWlZmyHfpO2O0soWFl8eyll+eSTTy5atGj55ZefNWuW0/jpW0QTLHHTmKAgyYXohTOaMQIEek7AtedoJlNBGb0f5OSll156k002WXPNNXUKpylzsRRs/GSjWUU5tQSsqUjItBOF6k+UfMFBu/322x944AHTOxv7dTb+r0vH1zi5qj5MAQ0EIAABCEAAAhBwBDJejQyiXoeiRPVRpOHoOGomZSpZl0M7YyHDpk8lix4CEIAABCDgCBR6EMMS1YdK04SCktKU1Jw5c9Zaay2/YzJLO5cEGUjvzJwQNfOjIPeHgNWvq8doxpyN/RYE97ROSqd3KaTkMNTO6EcxpYSU3rdBhgAEIAABCPSKgO8LqSMrHIZZdR5UqHca6wpdOoVJCWfjn8JPx1I2IW+vUDudnw7yUAikqkmJR4OiSpcTBfmh/tNkt4bKhZapzYKNn2ztUleYZfPPoaz4uTHZFckd+r+K676+J8HpzVKHN9100zXXXOOnjwwBCIyUgK67odxBRppJEofApBLQ9qQf+chHVlppJXcZ6tCVVIdab+6UdoXaoYIMiB9qsoUiTBuBcFG+COg+7/6c7JhII0H2FkUaJ7ugG2+88dprr3XG/EIAAhCAAAQgAIFpICAvCI96GiqaMkIAAhCAgAi4KamVV17Z9X3+NJQ07s+B8mer/L5SNkbStzclQvsEVEH6s/Oa/Lr6D0FOaUGaCzJZgk0NSeZpnZFEgAAEIAABCLRAQJ2v71+1cEZOMS4E5JAfcMAB5ro739v3wH2P3RXKtSX9Dv2Z8uJH1LXxqa3bnxKRbEmZ3vdKFep8Vgu95557LAoCBCAAAQhAYLIJaJn1b3/7Wyuj+sRCt+j6RzNwgnWaBT2HEDACaiSSrf24Qxdq7ccXXNtzUdwvLpnDxS8EIAABCEAAAhCAAAQgAAEIQGDyCGhK6je/+Y0/M+DK6DSSTfCnqiaPwwSXSDXo16M7dOV1lWtVbId+XUvJ1NAENw+KBgEIQAACEIDAGBFwT5PNZ5Mgt035dxoJvhdXKJfFKuhrHw7nC5hlTh9mXRpF1O/LL79cJgVsIAABCEAAApNBQK6A6wQnoziUYrwIOJdMeTZPzAS1zPEqC7mFAAQgAAEIQAACEIAABCAAAQhAoDwBN/DXPID2MNCvnkXZlgZOWT4pLMeXgOpaf8q/+zVBhzytG99qJecQgAAEIAABCEwYATlmzltzHrtK5xaWmQPfWnmHv6rMd0Mlu0P3a6V6Xb04yA9df/3111hjDbNEgAAERkFA3zhzNx0lzkU3CsKkCYGQwO233/700087vesHnWyugA79DtFPwbfx9cgQyBCw5uQEv9VZLKeMGtA7GCUECEAAAhCAAAQmmACj4wmuXIoGAQhAAAKOQGpKyufjZgZ8jZOl1wd0XKimpyQwSRVS6q3GqlWC+7NqtTz//5DfLzJzO15YEFNDhgIBAhCAAAQgMCICTEqMCOz4Jpt33X1XXF6cFVOyBZlgoc2FCqvKCtmyQwlO9gXlTIfOB3WhbgmLs5Fs78QU/NQ//dM/3XbbbZsXjBQgAIEMgZ/97GeLFi1yBlx0GVAEQWCIBB566CFbVaZO0L35p6595syZroN33aXOqEP9ac5Ov1LqV33l71UzZuTzM9AgH53QMSJgrUV5NtkJ4a9s1IT054xl4P50qKaoPwn6M6U7pHdwHPiFAAQgAAEIQGCyCTA6nuz6pXQQgAAEICAC/pSUJgc0D/D6JNPvfzT75BC5OQE3s+TrAdhPAqovy5g/n+P0fmjBzAzUEpz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"
    }
   },
   "cell_type": "markdown",
   "id": "edeb59f7",
   "metadata": {},
   "source": [
    "\n",
    "![484180fc6abc244116b30e57cb6c0cf5.jpg](attachment:484180fc6abc244116b30e57cb6c0cf5.jpg)\n",
    "\n",
    "图 3.2：字典查询：我们使用一个关键字，如某人的名字、一个域名或一个英文单词，访问一个字典的条目；字典的其他名字有映射、哈希表、哈希和关联数组。\n",
    "\n",
    "在电话簿中，我们用名字查找一个条目得到一个数字。当我们在浏览器中输入一个域名，计算机查找它得到一个IP 地址。一个词频表允许我们查一个词找出它在一个文本集合中的频率。在所有这些情况中，我们都是从名称映射到数字，而不是其他如列表那样的方式。总之，我们希望能够在任意类型的信息之间映射。[3.1](https://usyiyi.github.io/nlp-py-2e-zh/5.html#tab-linguistic-objects)列出了各种语言学对象以及它们的映射。\n",
    "\n",
    "表 3.1：\n",
    "\n",
    "语言学对象从键到值的映射"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4c6726ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos = {}\n",
    "pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "29837422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'colorless': 'ADJ'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos['colorless'] = 'ADJ' \n",
    "pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2c975672",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'colorless': 'ADJ', 'ideas': 'N', 'sleep': 'V', 'furiously': 'ADV'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos['ideas'] = 'N'\n",
    "pos['sleep'] = 'V'\n",
    "pos['furiously'] = 'ADV'\n",
    "pos "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e0697f4",
   "metadata": {},
   "source": [
    "所以，例如， [# 1](https://usyiyi.github.io/nlp-py-2e-zh/5.html#pos-colorless)说的是colorless的词性是形容词，或者更具体地说：在字典`pos`中，键`'colorless'`被分配了值`'ADJ'`。当我们检查`pos`的值时 [# 2](https://usyiyi.github.io/nlp-py-2e-zh/5.html#pos-inspect)，我们看到一个键-值对的集合。一旦我们以这样的方式填充了字典，就可以使用键来检索值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e3d17ba6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'N'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos['ideas']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6f19864f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ADJ'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos['colorless']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "950b7522",
   "metadata": {},
   "source": [
    "当然，我们可能会无意中使用一个尚未分配值的键。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9a85bb59",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'green'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_6288\\2405913685.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpos\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'green'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m: 'green'"
     ]
    }
   ],
   "source": [
    "pos['green']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9635283e",
   "metadata": {},
   "source": [
    "这就提出了一个重要的问题。与列表和字符串不同，我们可以用`len()`算出哪些整数是合法索引，我们如何算出一个字典的合法键？如果字典不是太大，我们可以简单地通过查看变量`pos`检查它的内容。正如在前面（ [# 2](https://usyiyi.github.io/nlp-py-2e-zh/5.html#pos-inspect)行）所看到，这为我们提供了键-值对。请注意它们的顺序与最初放入它们的顺序不同；这是因为字典不是序列而是映射（参见[3.2](https://usyiyi.github.io/nlp-py-2e-zh/5.html#fig-maps02)），键没有固定地排序。\n",
    "\n",
    "换种方式，要找到键，我们可以将字典转换成一个列表 [# 1](https://usyiyi.github.io/nlp-py-2e-zh/5.html#dict-to-list)——要么在期望列表的上下文中使用字典，如作为`sorted()`的参数 [# 2](https://usyiyi.github.io/nlp-py-2e-zh/5.html#dict-sorted)，要么在`for` 循环中 [# 3](https://usyiyi.github.io/nlp-py-2e-zh/5.html#dict-for-loop)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bc176b92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['colorless', 'ideas', 'sleep', 'furiously']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(pos) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "03c5b82d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['colorless', 'furiously', 'ideas', 'sleep']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(pos) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e9d09671",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['colorless', 'ideas']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[w for w in pos if w.endswith('s')] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1102405",
   "metadata": {},
   "source": [
    "注意\n",
    "\n",
    "当你输入`list(pos)`时，你看到的可能会与这里显示的顺序不同。如果你想看到有序的键，只需要对它们进行排序。\n",
    "\n",
    "与使用一个`for`循环遍历字典中的所有键一样，我们可以使用`for`循环输出列表："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e7c68f78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "colorless: ADJ\n",
      "furiously: ADV\n",
      "ideas: N\n",
      "sleep: V\n"
     ]
    }
   ],
   "source": [
    "for word in sorted(pos):\n",
    "   print(word + \":\", pos[word])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6a045fb",
   "metadata": {},
   "source": [
    "最后，字典的方法`keys()`、`values()`和`items()`允许我们以单独的列表访问键、值以及键-值对。我们甚至可以排序元组 [# 1](https://usyiyi.github.io/nlp-py-2e-zh/5.html#sort-tuples)，按它们的第一个元素排序（如果第一个元素相同，就使用它们的第二个元素）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ed0555d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['colorless', 'ideas', 'sleep', 'furiously']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(pos.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "72fb246c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ADJ', 'N', 'V', 'ADV']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(pos.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c35f2ed8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('colorless', 'ADJ'), ('ideas', 'N'), ('sleep', 'V'), ('furiously', 'ADV')]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(pos.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1c14e407",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "colorless: ADJ\n",
      "furiously: ADV\n",
      "ideas: N\n",
      "sleep: V\n"
     ]
    }
   ],
   "source": [
    "for key, val in sorted(pos.items()): \n",
    "   print(key + \":\", val)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a288acb7",
   "metadata": {},
   "source": [
    "我们要确保当我们在字典中查找某词时，一个键只得到一个值。现在假设我们试图用字典来存储可同时作为动词和名词的词sleep："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e9eadc7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'V'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos['sleep'] = 'V'\n",
    "pos['sleep']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "52f4596e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'N'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos['sleep'] = 'N'\n",
    "pos['sleep']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21f058ea",
   "metadata": {},
   "source": [
    "最初，`pos['sleep']`给的值是`'V'`。但是，它立即被一个新值`'N'`覆盖。换句话说，字典中只能有`'sleep'`的一个条目。然而，有一个方法可以在该项目中存储多个值：我们使用一个列表值，例如`pos['sleep'] = ['N', 'V']`。事实上，这就是我们在[4](https://usyiyi.github.io/nlp-py-2e-zh/2.html#sec-lexical-resources)中看到的CMU发音字典，它为一个词存储多个发音。\n",
    "\n",
    "## 3.3 定义字典\n",
    "\n",
    "我们可以使用键-值对格式创建字典。有两种方式做这个，我们通常会使用第一个："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8c2a4829",
   "metadata": {},
   "outputs": [],
   "source": [
    "pos = {'colorless': 'ADJ', 'ideas': 'N', 'sleep': 'V', 'furiously': 'ADV'}\n",
    "pos = dict(colorless='ADJ', ideas='N', sleep='V', furiously='ADV')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ebbc16c",
   "metadata": {},
   "source": [
    "请注意，字典的键必须是不可改变的类型，如字符串和元组。如果我们尝试使用可变键定义字典会得到一个`TypeError`："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8762fabf",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'list'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_6288\\474091498.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpos\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'ideas'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'blogs'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'adventures'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'N'\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: unhashable type: 'list'"
     ]
    }
   ],
   "source": [
    "pos = {['ideas', 'blogs', 'adventures']: 'N'}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b4c0fa6",
   "metadata": {},
   "source": [
    "## 3.4 默认字典\n",
    "\n",
    "如果我们试图访问一个不在字典中的键，会得到一个错误。然而，如果一个字典能为这个新键自动创建一个条目并给它一个默认值，如0或者一个空链表，将是有用的。由于这个原因，可以使用一种特殊的称为`defaultdict`的字典。为了使用它，我们必须提供一个参数，用来创建默认值，如`int`, `float`, `str`, `list`, `dict`, `tuple`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f926ca20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "frequency = defaultdict(int)\n",
    "frequency['colorless'] = 4\n",
    "frequency['ideas']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "285fe6f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos = defaultdict(list)\n",
    "pos['sleep'] = ['NOUN', 'VERB']\n",
    "pos['ideas']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44809f83",
   "metadata": {},
   "source": [
    "注意\n",
    "\n",
    "这些默认值实际上是将其他对象转换为指定类型的函数（例如`int(\"2\")`, `list(\"2\")`）。当它们不带参数被调用时——`int()`, `list()`——它们分别返回`0`和`[]` 。\n",
    "\n",
    "前面的例子中指定字典项的默认值为一个特定的数据类型的默认值。然而，也可以指定任何我们喜欢的默认值，只要提供可以无参数的被调用产生所需值的函数的名子。让我们回到我们的词性的例子，创建一个任一条目的默认值是`'N'`的字典 [# 1](https://usyiyi.github.io/nlp-py-2e-zh/5.html#default-noun)。当我们访问一个不存在的条目时 [# 2](https://usyiyi.github.io/nlp-py-2e-zh/5.html#non-existent)，它会自动添加到字典 [# 3](https://usyiyi.github.io/nlp-py-2e-zh/5.html#automatically-added)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3e07ddf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NOUN'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos = defaultdict(lambda: 'NOUN') \n",
    "pos['colorless'] = 'ADJ'\n",
    "pos['blog'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d8e5d49b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('colorless', 'ADJ'), ('blog', 'NOUN')]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(pos.items())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0502f3ae",
   "metadata": {},
   "source": [
    "注意\n",
    "\n",
    "上面的例子使用一个lambda表达式，在[4.4](https://usyiyi.github.io/nlp-py-2e-zh/4.html#sec-functions)介绍过。这个lambda表达式没有指定参数，所以我们用不带参数的括号调用它。因此，下面的`f`和`g`的定义是等价的："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b8ee0d0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NOUN'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = lambda: 'NOUN'\n",
    "f()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "eceee367",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NOUN'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def g():\n",
    "   return 'NOUN'\n",
    "g()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4a48eac",
   "metadata": {},
   "source": [
    "让我们来看看默认字典如何被应用在较大规模的语言处理任务中。许多语言处理任务——包括标注——费很大力气来正确处理文本中只出现过一次的词。如果有一个固定的词汇和没有新词会出现的保证，它们会有更好的表现。在一个默认字典的帮助下，我们可以预处理一个文本，替换低频词汇为一个特殊的“超出词汇表”词符`UNK`。（你能不看下面的想出如何做吗？）\n",
    "\n",
    "我们需要创建一个默认字典，映射每个词为它们的替换词。最频繁的n个词将被映射到它们自己。其他的被映射到`UNK`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e35531c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['[',\n",
       " 'Alice',\n",
       " \"'\",\n",
       " 's',\n",
       " 'Adventures',\n",
       " 'in',\n",
       " 'Wonderland',\n",
       " 'by',\n",
       " 'UNK',\n",
       " 'UNK',\n",
       " 'UNK',\n",
       " 'UNK',\n",
       " 'CHAPTER',\n",
       " 'I',\n",
       " '.',\n",
       " 'Down',\n",
       " 'the',\n",
       " 'Rabbit',\n",
       " '-',\n",
       " 'UNK',\n",
       " 'Alice',\n",
       " 'was',\n",
       " 'beginning',\n",
       " 'to',\n",
       " 'get',\n",
       " 'very',\n",
       " 'tired',\n",
       " 'of',\n",
       " 'sitting',\n",
       " 'by',\n",
       " 'her',\n",
       " 'sister',\n",
       " 'on',\n",
       " 'the',\n",
       " 'bank',\n",
       " ',',\n",
       " 'and',\n",
       " 'of',\n",
       " 'having',\n",
       " 'nothing',\n",
       " 'to',\n",
       " 'do',\n",
       " ':',\n",
       " 'once',\n",
       " 'or',\n",
       " 'twice',\n",
       " 'she',\n",
       " 'had',\n",
       " 'peeped',\n",
       " 'into',\n",
       " 'the',\n",
       " 'book',\n",
       " 'her',\n",
       " 'sister',\n",
       " 'was',\n",
       " 'reading',\n",
       " ',',\n",
       " 'but',\n",
       " 'it',\n",
       " 'had',\n",
       " 'no',\n",
       " 'pictures',\n",
       " 'or',\n",
       " 'UNK',\n",
       " 'in',\n",
       " 'it',\n",
       " ',',\n",
       " \"'\",\n",
       " 'and',\n",
       " 'what',\n",
       " 'is',\n",
       " 'the',\n",
       " 'use',\n",
       " 'of',\n",
       " 'a',\n",
       " 'book',\n",
       " \",'\",\n",
       " 'thought',\n",
       " 'Alice',\n",
       " \"'\",\n",
       " 'without',\n",
       " 'pictures',\n",
       " 'or',\n",
       " 'conversation',\n",
       " \"?'\",\n",
       " 'So',\n",
       " 'she',\n",
       " 'was',\n",
       " 'considering',\n",
       " 'in',\n",
       " 'her',\n",
       " 'own',\n",
       " 'mind',\n",
       " '(',\n",
       " 'as',\n",
       " 'well',\n",
       " 'as',\n",
       " 'she',\n",
       " 'could',\n",
       " ',']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk # 由于图片内嵌问题而重新引入 nltk，如果之前 import nltk 未中断则不需要此步骤\n",
    "\n",
    "alice = nltk.corpus.gutenberg.words('carroll-alice.txt')\n",
    "vocab = nltk.FreqDist(alice)\n",
    "v1000 = [word for (word, _) in vocab.most_common(1000)]\n",
    "mapping = defaultdict(lambda: 'UNK')\n",
    "for v in v1000:\n",
    "   mapping[v] = v\n",
    "\n",
    "alice2 = [mapping[v] for v in alice]\n",
    "alice2[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3a875074",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1001"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(alice2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1923d71f",
   "metadata": {},
   "source": [
    "## 3.5 递增地更新字典\n",
    "\n",
    "我们可以使用字典计数出现的次数，模拟[fig-tally](https://usyiyi.github.io/nlp-py-2e-zh/1.html#fig-tally)所示的计数词汇的方法。首先初始化一个空的`defaultdict`，然后处理文本中每个词性标记。如果标记以前没有见过，就默认计数为零。每次我们遇到一个标记，就使用`+=`运算符递增它的计数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "933cffae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30654"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "counts = defaultdict(int)\n",
    "from nltk.corpus import brown\n",
    "for (word, tag) in brown.tagged_words(categories='news', tagset='universal'):\n",
    "   counts[tag] += 1\n",
    "\n",
    "counts['NOUN']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e50cb1de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.',\n",
       " 'ADJ',\n",
       " 'ADP',\n",
       " 'ADV',\n",
       " 'CONJ',\n",
       " 'DET',\n",
       " 'NOUN',\n",
       " 'NUM',\n",
       " 'PRON',\n",
       " 'PRT',\n",
       " 'VERB',\n",
       " 'X']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "be01a8a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('NOUN', 30654),\n",
       " ('VERB', 14399),\n",
       " ('ADP', 12355),\n",
       " ('.', 11928),\n",
       " ('DET', 11389),\n",
       " ('ADJ', 6706),\n",
       " ('ADV', 3349),\n",
       " ('CONJ', 2717),\n",
       " ('PRON', 2535),\n",
       " ('PRT', 2264),\n",
       " ('NUM', 2166),\n",
       " ('X', 92)]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from operator import itemgetter\n",
    "sorted(counts.items(), key=itemgetter(1), reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d11bd807",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NOUN',\n",
       " 'VERB',\n",
       " 'ADP',\n",
       " '.',\n",
       " 'DET',\n",
       " 'ADJ',\n",
       " 'ADV',\n",
       " 'CONJ',\n",
       " 'PRON',\n",
       " 'PRT',\n",
       " 'NUM',\n",
       " 'X']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[t for t, c in sorted(counts.items(), key=itemgetter(1), reverse=True)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7299713e",
   "metadata": {},
   "source": [
    "[3.3](https://usyiyi.github.io/nlp-py-2e-zh/5.html#code-dictionary)中的列表演示了一个重要的按值排序一个字典的习惯用法，来按频率递减顺序显示词汇。`sorted()`的第一个参数是要排序的项目，它是由一个词性标记和一个频率组成的元组的列表。第二个参数使用函数`itemgetter()`指定排序的键。在一般情况下，`itemgetter(n)`返回一个函数，这个函数可以在一些其他序列对象上被调用获得这个序列的第n个元素，例如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "368973fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8336"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair = ('NP', 8336)\n",
    "pair[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "45fa96ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8336"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "itemgetter(1)(pair)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a313676a",
   "metadata": {},
   "source": [
    "`sorted()`的最后一个参数指定项目是否应被按相反的顺序返回，即频率值递减。\n",
    "\n",
    "在[3.3](https://usyiyi.github.io/nlp-py-2e-zh/5.html#code-dictionary)的开头还有第二个有用的习惯用法，那里我们初始化一个`defaultdict`，然后使用`for`循环来更新其值。下面是一个示意版本："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94d84256",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_dictionary = defaultdict(function to create default value) # 参考代码，不需要执行\n",
    "for item in sequence:\n",
    "    my_dictionary[item_key] is updated with information about item"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c672268",
   "metadata": {},
   "source": [
    "`my_dictionary[`*item_key*`]` *is updated with information about item*\n",
    "\n",
    "下面是这种模式的另一个示例，我们按它们最后两个字母索引词汇："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7d537e63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['abactinally',\n",
       " 'abandonedly',\n",
       " 'abasedly',\n",
       " 'abashedly',\n",
       " 'abashlessly',\n",
       " 'abbreviately',\n",
       " 'abdominally',\n",
       " 'abhorrently',\n",
       " 'abidingly',\n",
       " 'abiogenetically',\n",
       " 'abiologically',\n",
       " 'abjectly',\n",
       " 'ableptically',\n",
       " 'ably',\n",
       " 'abnormally',\n",
       " 'abominably',\n",
       " 'aborally',\n",
       " 'aboriginally',\n",
       " 'abortively',\n",
       " 'aboundingly',\n",
       " 'abridgedly',\n",
       " 'abruptedly',\n",
       " 'abruptly',\n",
       " 'abscondedly',\n",
       " 'absently',\n",
       " 'absentmindedly',\n",
       " 'absolutely',\n",
       " 'absolutistically',\n",
       " 'absorbedly',\n",
       " 'absorbingly',\n",
       " 'absorptively',\n",
       " 'abstemiously',\n",
       " 'abstinently',\n",
       " 'abstractedly',\n",
       " 'abstractively',\n",
       " 'abstractly',\n",
       " 'abstrusely',\n",
       " 'absurdly',\n",
       " 'abundantly',\n",
       " 'abusedly',\n",
       " 'abusefully',\n",
       " 'abusively',\n",
       " 'abysmally',\n",
       " 'academically',\n",
       " 'acceleratedly',\n",
       " 'accentually',\n",
       " 'acceptably',\n",
       " 'acceptedly',\n",
       " 'accessarily',\n",
       " 'accessibly',\n",
       " 'accessively',\n",
       " 'accessorily',\n",
       " 'accidentally',\n",
       " 'accidently',\n",
       " 'accommodately',\n",
       " 'accommodatingly',\n",
       " 'accordantly',\n",
       " 'accordingly',\n",
       " 'accountably',\n",
       " 'accumulatively',\n",
       " 'accurately',\n",
       " 'accursedly',\n",
       " 'accusably',\n",
       " 'accusatively',\n",
       " 'accusatorially',\n",
       " 'accusingly',\n",
       " 'accustomedly',\n",
       " 'acervately',\n",
       " 'acetometrically',\n",
       " 'achingly',\n",
       " 'achromatically',\n",
       " 'acicularly',\n",
       " 'acidimetrically',\n",
       " 'acidly',\n",
       " 'acknowledgedly',\n",
       " 'acoustically',\n",
       " 'acquiescently',\n",
       " 'acquiescingly',\n",
       " 'acquisitively',\n",
       " 'acridly',\n",
       " 'acrimoniously',\n",
       " 'acrobatically',\n",
       " 'acrocephaly',\n",
       " 'acrogenously',\n",
       " 'acrologically',\n",
       " 'acromegaly',\n",
       " 'acronically',\n",
       " 'acropetally',\n",
       " 'acrostically',\n",
       " 'actinally',\n",
       " 'actinically',\n",
       " 'actinoelectrically',\n",
       " 'actionably',\n",
       " 'actively',\n",
       " 'actually',\n",
       " 'actuarially',\n",
       " 'acutely',\n",
       " 'Adamically',\n",
       " 'adaptationally',\n",
       " 'adaptively',\n",
       " 'addedly',\n",
       " 'additionally',\n",
       " 'additively',\n",
       " 'addleheadedly',\n",
       " 'adequately',\n",
       " 'adherently',\n",
       " 'adhesively',\n",
       " 'adiabatically',\n",
       " 'adjacently',\n",
       " 'adjectivally',\n",
       " 'adjectively',\n",
       " 'adjoinedly',\n",
       " 'adjunctively',\n",
       " 'adjunctly',\n",
       " 'adjustably',\n",
       " 'administratively',\n",
       " 'admirably',\n",
       " 'admiredly',\n",
       " 'admiringly',\n",
       " 'admissibly',\n",
       " 'admittedly',\n",
       " 'admonishingly',\n",
       " 'admonitively',\n",
       " 'admonitorily',\n",
       " 'adnominally',\n",
       " 'adolescently',\n",
       " 'adoptedly',\n",
       " 'adoptively',\n",
       " 'adorably',\n",
       " 'adorally',\n",
       " 'adoringly',\n",
       " 'adorningly',\n",
       " 'adradially',\n",
       " 'adroitly',\n",
       " 'adscititiously',\n",
       " 'adulterately',\n",
       " 'adulterously',\n",
       " 'adumbratively',\n",
       " 'advancingly',\n",
       " 'advantageously',\n",
       " 'adventitiously',\n",
       " 'adventuresomely',\n",
       " 'adventurously',\n",
       " 'adverbially',\n",
       " 'adversatively',\n",
       " 'adversely',\n",
       " 'advertently',\n",
       " 'advisably',\n",
       " 'advisedly',\n",
       " 'advisorily',\n",
       " 'aerially',\n",
       " 'aerobically',\n",
       " 'aerobiologically',\n",
       " 'aerobiotically',\n",
       " 'aerogenically',\n",
       " 'aeronautically',\n",
       " 'aerophilately',\n",
       " 'aeroscopically',\n",
       " 'aesthetically',\n",
       " 'aetiotropically',\n",
       " 'affably',\n",
       " 'affectedly',\n",
       " 'affectingly',\n",
       " 'affectionally',\n",
       " 'affectionately',\n",
       " 'affectively',\n",
       " 'affinely',\n",
       " 'affinitatively',\n",
       " 'affirmably',\n",
       " 'affirmatively',\n",
       " 'affirmingly',\n",
       " 'afflictingly',\n",
       " 'afflictively',\n",
       " 'affluently',\n",
       " 'affrightedly',\n",
       " 'affrightfully',\n",
       " 'affrightingly',\n",
       " 'affrontedly',\n",
       " 'affrontingly',\n",
       " 'agamically',\n",
       " 'agamogenetically',\n",
       " 'agedly',\n",
       " 'aggravatingly',\n",
       " 'aggregately',\n",
       " 'aggressively',\n",
       " 'aggrievedly',\n",
       " 'agilely',\n",
       " 'agitatedly',\n",
       " 'agnatically',\n",
       " 'agnostically',\n",
       " 'agonistically',\n",
       " 'agonizedly',\n",
       " 'agonizingly',\n",
       " 'agrarianly',\n",
       " 'agreeably',\n",
       " 'agreeingly',\n",
       " 'agriculturally',\n",
       " 'agrobiologically',\n",
       " 'agrogeologically',\n",
       " 'agrologically',\n",
       " 'aguishly',\n",
       " 'aimfully',\n",
       " 'aimlessly',\n",
       " 'airily',\n",
       " 'airtightly',\n",
       " 'alarmedly',\n",
       " 'alarmingly',\n",
       " 'alchemically',\n",
       " 'alcoholically',\n",
       " 'aldermanly',\n",
       " 'alertly',\n",
       " 'algebraically',\n",
       " 'algometrically',\n",
       " 'alimentally',\n",
       " 'alimentatively',\n",
       " 'alkalimetrically',\n",
       " 'allegedly',\n",
       " 'allegorically',\n",
       " 'allenarly',\n",
       " 'alleviatingly',\n",
       " 'alliably',\n",
       " 'allicholly',\n",
       " 'alliteratively',\n",
       " 'allochirally',\n",
       " 'allogenically',\n",
       " 'allopathetically',\n",
       " 'allopathically',\n",
       " 'allopatrically',\n",
       " 'allothigenetically',\n",
       " 'allotropically',\n",
       " 'allowably',\n",
       " 'allowedly',\n",
       " 'alluringly',\n",
       " 'allusively',\n",
       " 'Ally',\n",
       " 'ally',\n",
       " 'almightily',\n",
       " 'alodially',\n",
       " 'alogically',\n",
       " 'aloofly',\n",
       " 'alphabetically',\n",
       " 'alpinely',\n",
       " 'alterably',\n",
       " 'alternately',\n",
       " 'alternatingly',\n",
       " 'alternatively',\n",
       " 'altimetrically',\n",
       " 'altruistically',\n",
       " 'aly',\n",
       " 'amateurishly',\n",
       " 'amatively',\n",
       " 'amatorially',\n",
       " 'amazedly',\n",
       " 'amazingly',\n",
       " 'ambagiously',\n",
       " 'ambassadorially',\n",
       " 'ambidextrously',\n",
       " 'ambiguously',\n",
       " 'ambilateralaterally',\n",
       " 'ambitionlessly',\n",
       " 'ambitiously',\n",
       " 'amblingly',\n",
       " 'ambrosially',\n",
       " 'amenably',\n",
       " 'Americanly',\n",
       " 'ametaboly',\n",
       " 'amethodically',\n",
       " 'amiably',\n",
       " 'amicably',\n",
       " 'amitotically',\n",
       " 'amorously',\n",
       " 'amorphously',\n",
       " 'amphibiously',\n",
       " 'amphibologically',\n",
       " 'amphiboly',\n",
       " 'amphigenously',\n",
       " 'amphimictically',\n",
       " 'amphistyly',\n",
       " 'amphitheatrically',\n",
       " 'amply',\n",
       " 'amusedly',\n",
       " 'amusingly',\n",
       " 'amusively',\n",
       " 'Anabaptistically',\n",
       " 'anacamptically',\n",
       " 'anachronically',\n",
       " 'anachronistically',\n",
       " 'anachronously',\n",
       " 'anacoluthically',\n",
       " 'Anacreontically',\n",
       " 'anacrustically',\n",
       " 'anaerobically',\n",
       " 'anaerobiotically',\n",
       " 'anaesthetically',\n",
       " 'anagogically',\n",
       " 'anagrammatically',\n",
       " 'anally',\n",
       " 'analogically',\n",
       " 'analogously',\n",
       " 'analytically',\n",
       " 'anamnestically',\n",
       " 'anapaestically',\n",
       " 'anarchically',\n",
       " 'anarthrously',\n",
       " 'anathematically',\n",
       " 'Anatoly',\n",
       " 'anatomically',\n",
       " 'ancestorially',\n",
       " 'ancestrally',\n",
       " 'anciently',\n",
       " 'anecdotically',\n",
       " 'anemographically',\n",
       " 'anemometrically',\n",
       " 'anemometrographically',\n",
       " 'anemophily',\n",
       " 'anencephaly',\n",
       " 'anerly',\n",
       " 'anesthetically',\n",
       " 'aneurismally',\n",
       " 'aneurysmally',\n",
       " 'angelically',\n",
       " 'angerly',\n",
       " 'angiomegaly',\n",
       " 'Anglicanly',\n",
       " 'angrily',\n",
       " 'anguishously',\n",
       " 'angularly',\n",
       " 'angulately',\n",
       " 'animally',\n",
       " 'animatedly',\n",
       " 'animately',\n",
       " 'animatingly',\n",
       " 'anisocotyly',\n",
       " 'anisophylly',\n",
       " 'anisotropically',\n",
       " 'anniversarily',\n",
       " 'annoyingly',\n",
       " 'annually',\n",
       " 'annularly',\n",
       " 'anodically',\n",
       " 'anomalistically',\n",
       " 'anomalously',\n",
       " 'anomaly',\n",
       " 'anonymously',\n",
       " 'anorthographically',\n",
       " 'answerably',\n",
       " 'answeringly',\n",
       " 'answerlessly',\n",
       " 'antagonistically',\n",
       " 'antarctically',\n",
       " 'antecedaneously',\n",
       " 'antecedently',\n",
       " 'antediluvially',\n",
       " 'anteriorly',\n",
       " 'anterolaterally',\n",
       " 'anteroposteriorly',\n",
       " 'anteroventrally',\n",
       " 'anthologically',\n",
       " 'anthropologically',\n",
       " 'anthropometrically',\n",
       " 'anthropomorphically',\n",
       " 'anthropomorphologically',\n",
       " 'anthropomorphously',\n",
       " 'anthropopathically',\n",
       " 'anthropophagously',\n",
       " 'antichristianly',\n",
       " 'antichronically',\n",
       " 'anticipatively',\n",
       " 'anticipatorily',\n",
       " 'anticly',\n",
       " 'anticonstitutionally',\n",
       " 'anticyclonically',\n",
       " 'antidotally',\n",
       " 'antidotically',\n",
       " 'antidromically',\n",
       " 'antimonarchically',\n",
       " 'antimonopoly',\n",
       " 'antipathetically',\n",
       " 'antiperistatically',\n",
       " 'antiphonally',\n",
       " 'antiphonically',\n",
       " 'antiphrastically',\n",
       " 'antiquarianly',\n",
       " 'antiquely',\n",
       " 'antirachitically',\n",
       " 'antiseptically',\n",
       " 'antisocialistically',\n",
       " 'antistrophically',\n",
       " 'antitheistically',\n",
       " 'antithetically',\n",
       " 'antitypically',\n",
       " 'antonomastically',\n",
       " 'antrorsely',\n",
       " 'anxiously',\n",
       " 'aoristically',\n",
       " 'apagogically',\n",
       " 'apathetically',\n",
       " 'aperiodically',\n",
       " 'apertly',\n",
       " 'apetaly',\n",
       " 'apheliotropically',\n",
       " 'aphetically',\n",
       " 'aphoristically',\n",
       " 'aphylly',\n",
       " 'apically',\n",
       " 'apishly',\n",
       " 'aplanatically',\n",
       " 'apocalyptically',\n",
       " 'apocryphally',\n",
       " 'apodictically',\n",
       " 'apogamically',\n",
       " 'apogamously',\n",
       " 'apogeotropically',\n",
       " 'apologetically',\n",
       " 'apometaboly',\n",
       " 'apoplectically',\n",
       " 'aposematically',\n",
       " 'apostatically',\n",
       " 'apostolically',\n",
       " 'apothegmatically',\n",
       " 'appallingly',\n",
       " 'apparently',\n",
       " 'appealingly',\n",
       " 'appeasably',\n",
       " 'appeasingly',\n",
       " 'appellatively',\n",
       " 'apperceptively',\n",
       " 'appetently',\n",
       " 'appetizingly',\n",
       " 'applaudably',\n",
       " 'applaudingly',\n",
       " 'applausively',\n",
       " 'appliably',\n",
       " 'applicably',\n",
       " 'applicatively',\n",
       " 'applicatorily',\n",
       " 'appliedly',\n",
       " 'apply',\n",
       " 'applyingly',\n",
       " 'appositely',\n",
       " 'appositionally',\n",
       " 'appositively',\n",
       " 'appraisingly',\n",
       " 'appreciably',\n",
       " 'appreciatingly',\n",
       " 'appreciatively',\n",
       " 'appreciatorily',\n",
       " 'apprehendingly',\n",
       " 'apprehensibly',\n",
       " 'apprehensively',\n",
       " 'appropriately',\n",
       " 'approvedly',\n",
       " 'approvingly',\n",
       " 'approximately',\n",
       " 'approximatively',\n",
       " 'appulsively',\n",
       " 'apsidally',\n",
       " 'aptitudinally',\n",
       " 'aptly',\n",
       " 'aquatically',\n",
       " 'aqueously',\n",
       " 'arabesquely',\n",
       " 'arbitrarily',\n",
       " 'arboreally',\n",
       " 'arborescently',\n",
       " 'Arcadianly',\n",
       " 'archaeologically',\n",
       " 'archaically',\n",
       " 'archetypally',\n",
       " 'archetypically',\n",
       " 'archiepiscopally',\n",
       " 'architectonically',\n",
       " 'architecturally',\n",
       " 'archly',\n",
       " 'arctically',\n",
       " 'arcuately',\n",
       " 'ardently',\n",
       " 'arduously',\n",
       " 'areographically',\n",
       " 'areologically',\n",
       " 'argentometrically',\n",
       " 'argumentatively',\n",
       " 'argutely',\n",
       " 'aridly',\n",
       " 'arightly',\n",
       " 'aristocratically',\n",
       " 'arithmetically',\n",
       " 'aromatically',\n",
       " 'arrantly',\n",
       " 'arrestingly',\n",
       " 'arrhythmically',\n",
       " 'arrogantly',\n",
       " 'arrogatingly',\n",
       " 'arterially',\n",
       " 'artfully',\n",
       " 'articularly',\n",
       " 'articulately',\n",
       " 'artificially',\n",
       " 'artistically',\n",
       " 'artlessly',\n",
       " 'ascendingly',\n",
       " 'ascertainably',\n",
       " 'ascetically',\n",
       " 'aseptically',\n",
       " 'asexually',\n",
       " 'ashamedly',\n",
       " 'ashily',\n",
       " 'Asiatically',\n",
       " 'asininely',\n",
       " 'askingly',\n",
       " 'asperously',\n",
       " 'aspersively',\n",
       " 'aspiringly',\n",
       " 'assembly',\n",
       " 'assentatorily',\n",
       " 'assentingly',\n",
       " 'assertively',\n",
       " 'assertorially',\n",
       " 'assertorically',\n",
       " 'assertorily',\n",
       " 'assessably',\n",
       " 'asseveratingly',\n",
       " 'asseveratively',\n",
       " 'assidually',\n",
       " 'assiduously',\n",
       " 'assignably',\n",
       " 'assishly',\n",
       " 'associatively',\n",
       " 'assumably',\n",
       " 'assumedly',\n",
       " 'assumingly',\n",
       " 'assumptively',\n",
       " 'assuredly',\n",
       " 'assuringly',\n",
       " 'astatically',\n",
       " 'astely',\n",
       " 'asthmatically',\n",
       " 'astigmatically',\n",
       " 'astonishedly',\n",
       " 'astonishingly',\n",
       " 'astoundingly',\n",
       " 'astrally',\n",
       " 'astrictively',\n",
       " 'astringently',\n",
       " 'astrologically',\n",
       " 'astronomically',\n",
       " 'astuciously',\n",
       " 'astutely',\n",
       " 'asymmetrically',\n",
       " 'asymptotically',\n",
       " 'asyndetically',\n",
       " 'atavistically',\n",
       " 'atheistically',\n",
       " 'Athenianly',\n",
       " 'atheologically',\n",
       " 'athletically',\n",
       " 'atmospherically',\n",
       " 'atomically',\n",
       " 'atomistically',\n",
       " 'atonally',\n",
       " 'atoningly',\n",
       " 'atrociously',\n",
       " 'attachedly',\n",
       " 'attemperately',\n",
       " 'attendantly',\n",
       " 'attendingly',\n",
       " 'attentively',\n",
       " 'attently',\n",
       " 'attractingly',\n",
       " 'attractionally',\n",
       " 'attractively',\n",
       " 'attributively',\n",
       " 'attunely',\n",
       " 'atypically',\n",
       " 'audaciously',\n",
       " 'audibly',\n",
       " 'auditorially',\n",
       " 'auditorily',\n",
       " 'augmentatively',\n",
       " 'augmentedly',\n",
       " 'augustly',\n",
       " 'auntly',\n",
       " 'aurally',\n",
       " 'aureately',\n",
       " 'aureously',\n",
       " 'auricularly',\n",
       " 'auriculately',\n",
       " 'aurorally',\n",
       " 'auspiciously',\n",
       " 'austerely',\n",
       " 'auteciously',\n",
       " 'autecologically',\n",
       " 'authentically',\n",
       " 'authenticly',\n",
       " 'authorially',\n",
       " 'authoritatively',\n",
       " 'authorly',\n",
       " 'autobiographically',\n",
       " 'autocatalytically',\n",
       " 'autocephaly',\n",
       " 'autochthonously',\n",
       " 'autocratically',\n",
       " 'autoeciously',\n",
       " 'autoerotically',\n",
       " 'autogenetically',\n",
       " 'autogenously',\n",
       " 'autographically',\n",
       " 'automatically',\n",
       " 'automorphically',\n",
       " 'autonomically',\n",
       " 'autonomously',\n",
       " 'autophytically',\n",
       " 'autoptically',\n",
       " 'autoschediastically',\n",
       " 'autostyly',\n",
       " 'autosymbolically',\n",
       " 'autotropically',\n",
       " 'autumnally',\n",
       " 'auxetically',\n",
       " 'auxiliarly',\n",
       " 'auxinically',\n",
       " 'availably',\n",
       " 'availingly',\n",
       " 'avariciously',\n",
       " 'avengingly',\n",
       " 'averagely',\n",
       " 'aversely',\n",
       " 'avertedly',\n",
       " 'avidiously',\n",
       " 'avidly',\n",
       " 'avoidably',\n",
       " 'avowably',\n",
       " 'avowedly',\n",
       " 'awakeningly',\n",
       " 'awesomely',\n",
       " 'awfully',\n",
       " 'awkwardly',\n",
       " 'axially',\n",
       " 'axiologically',\n",
       " 'axiomatically',\n",
       " 'azimuthally',\n",
       " 'babblingly',\n",
       " 'babblishly',\n",
       " 'babbly',\n",
       " 'babishly',\n",
       " 'babyishly',\n",
       " 'bacchanalianly',\n",
       " 'bachelorly',\n",
       " 'backbitingly',\n",
       " 'backhandedly',\n",
       " 'backwardly',\n",
       " 'bacterially',\n",
       " 'bacteriologically',\n",
       " 'bacterioscopically',\n",
       " 'baddishly',\n",
       " 'badgeringly',\n",
       " 'badgerly',\n",
       " 'badly',\n",
       " 'bafflingly',\n",
       " 'baggily',\n",
       " 'bairnly',\n",
       " 'bakerly',\n",
       " 'bakingly',\n",
       " 'baldly',\n",
       " 'balefully',\n",
       " 'balkingly',\n",
       " 'ballistically',\n",
       " 'bally',\n",
       " 'balmily',\n",
       " 'balsamically',\n",
       " 'banally',\n",
       " 'bandlessly',\n",
       " 'banefully',\n",
       " 'bankruptly',\n",
       " 'banteringly',\n",
       " 'baptismally',\n",
       " 'barbarically',\n",
       " 'barbarously',\n",
       " 'bardily',\n",
       " 'barefacedly',\n",
       " 'barely',\n",
       " 'barfly',\n",
       " 'barkingly',\n",
       " 'barometrically',\n",
       " 'barratrously',\n",
       " 'barrenly',\n",
       " 'barruly',\n",
       " 'basally',\n",
       " 'baselessly',\n",
       " 'basely',\n",
       " 'bashfully',\n",
       " 'basically',\n",
       " 'bastardly',\n",
       " 'bathymetrically',\n",
       " 'bawdily',\n",
       " 'bayardly',\n",
       " 'beadily',\n",
       " 'beamily',\n",
       " 'beamingly',\n",
       " 'bearably',\n",
       " 'bearishly',\n",
       " 'beastily',\n",
       " 'beastlily',\n",
       " 'beastly',\n",
       " 'beatifically',\n",
       " 'beauteously',\n",
       " 'beautifully',\n",
       " 'beckoningly',\n",
       " 'becomingly',\n",
       " 'bedazzlingly',\n",
       " 'beefily',\n",
       " 'beerily',\n",
       " 'beerishly',\n",
       " 'befittingly',\n",
       " 'beggarly',\n",
       " 'beggingly',\n",
       " 'begrudgingly',\n",
       " 'beguilingly',\n",
       " 'behavioristically',\n",
       " 'behoovefully',\n",
       " 'behoovingly',\n",
       " 'beknottedly',\n",
       " 'belatedly',\n",
       " 'believingly',\n",
       " 'bellicosely',\n",
       " 'belligerently',\n",
       " 'belly',\n",
       " 'bely',\n",
       " 'belyingly',\n",
       " 'bemoaningly',\n",
       " 'bemusedly',\n",
       " 'bendingly',\n",
       " 'benedictively',\n",
       " 'beneficently',\n",
       " 'beneficially',\n",
       " 'benevolently',\n",
       " 'benignantly',\n",
       " 'benignly',\n",
       " 'benumbingly',\n",
       " 'beseechingly',\n",
       " 'beseemingly',\n",
       " 'beseemly',\n",
       " 'besiegingly',\n",
       " 'besottedly',\n",
       " 'besottingly',\n",
       " 'bestially',\n",
       " 'besully',\n",
       " 'betterly',\n",
       " 'Beverly',\n",
       " 'bewailingly',\n",
       " 'bewilderedly',\n",
       " 'bewilderingly',\n",
       " 'bewitchingly',\n",
       " 'bewrayingly',\n",
       " 'biannually',\n",
       " 'biaxially',\n",
       " 'Biblically',\n",
       " 'bibliographically',\n",
       " 'bibliophily',\n",
       " 'bibliopolically',\n",
       " 'bibliopoly',\n",
       " 'bibulously',\n",
       " 'biconically',\n",
       " 'biddably',\n",
       " 'bienly',\n",
       " 'biennially',\n",
       " 'bifariously',\n",
       " 'bifidly',\n",
       " 'bifilarly',\n",
       " 'bifurcately',\n",
       " 'bigamously',\n",
       " 'bigotedly',\n",
       " 'bihourly',\n",
       " 'bilaterally',\n",
       " 'bilingually',\n",
       " 'biliously',\n",
       " 'billiardly',\n",
       " 'Billy',\n",
       " 'billy',\n",
       " 'bimanually',\n",
       " 'bimonthly',\n",
       " 'binately',\n",
       " 'bindingly',\n",
       " 'binocularly',\n",
       " 'binomially',\n",
       " 'biochemically',\n",
       " 'bioecologically',\n",
       " 'biogenetically',\n",
       " 'biogeographically',\n",
       " 'biographically',\n",
       " 'biologically',\n",
       " 'biometrically',\n",
       " 'bionomically',\n",
       " 'bipartitely',\n",
       " 'bipinnately',\n",
       " 'biquarterly',\n",
       " 'bisectionally',\n",
       " 'biserially',\n",
       " 'biseriately',\n",
       " 'bisexually',\n",
       " 'bisymmetrically',\n",
       " 'biternately',\n",
       " 'bitingly',\n",
       " 'bitterly',\n",
       " 'biweekly',\n",
       " 'biyearly',\n",
       " 'bizarrely',\n",
       " 'blackbelly',\n",
       " 'blackguardly',\n",
       " 'blackishly',\n",
       " 'blackly',\n",
       " 'blamably',\n",
       " 'blamefully',\n",
       " 'blamelessly',\n",
       " 'blamingly',\n",
       " 'blanchingly',\n",
       " 'blandishingly',\n",
       " 'blandly',\n",
       " 'blankly',\n",
       " 'blasphemously',\n",
       " 'blatantly',\n",
       " 'blately',\n",
       " 'blazingly',\n",
       " 'bleakly',\n",
       " 'bleatingly',\n",
       " 'blenchingly',\n",
       " 'blessedly',\n",
       " 'blessingly',\n",
       " 'blightingly',\n",
       " 'blindedly',\n",
       " 'blindfoldly',\n",
       " 'blindingly',\n",
       " 'blindly',\n",
       " 'blinkingly',\n",
       " 'blissfully',\n",
       " 'blisteringly',\n",
       " 'blithefully',\n",
       " 'blithely',\n",
       " 'blithesomely',\n",
       " 'blizzardly',\n",
       " 'blockheadedly',\n",
       " 'blockishly',\n",
       " 'blolly',\n",
       " 'bloodily',\n",
       " 'bloodlessly',\n",
       " 'bloodthirstily',\n",
       " 'bloomingly',\n",
       " 'blottesquely',\n",
       " 'blottingly',\n",
       " 'blowfly',\n",
       " 'blubberingly',\n",
       " 'bluely',\n",
       " 'bluffly',\n",
       " 'blunderingly',\n",
       " 'bluntly',\n",
       " 'blushfully',\n",
       " 'blushingly',\n",
       " 'blusteringly',\n",
       " 'blusterously',\n",
       " 'boardly',\n",
       " 'boarishly',\n",
       " 'boastfully',\n",
       " 'boatly',\n",
       " 'bobbishly',\n",
       " 'bobfly',\n",
       " 'bodaciously',\n",
       " 'bodily',\n",
       " 'bodingly',\n",
       " 'boilingly',\n",
       " 'boily',\n",
       " 'boisterously',\n",
       " 'boldly',\n",
       " 'bolly',\n",
       " 'bolographically',\n",
       " 'Bolshevistically',\n",
       " 'bombastically',\n",
       " 'bonairly',\n",
       " 'bonally',\n",
       " 'bonelessly',\n",
       " 'bonnily',\n",
       " 'boobily',\n",
       " 'bookishly',\n",
       " 'booly',\n",
       " 'boomingly',\n",
       " 'boorishly',\n",
       " 'bootlessly',\n",
       " 'boozily',\n",
       " 'boringly',\n",
       " 'botanically',\n",
       " 'botchedly',\n",
       " 'botcherly',\n",
       " 'botchily',\n",
       " 'botfly',\n",
       " 'botryoidally',\n",
       " 'bottomlessly',\n",
       " 'bounceably',\n",
       " 'bouncingly',\n",
       " 'boundedly',\n",
       " 'boundingly',\n",
       " 'boundlessly',\n",
       " 'boundly',\n",
       " 'bounteously',\n",
       " 'bountifully',\n",
       " 'bovinely',\n",
       " 'bowingly',\n",
       " 'bowly',\n",
       " 'boyishly',\n",
       " 'brabblingly',\n",
       " 'brachistocephaly',\n",
       " 'brachycephaly',\n",
       " 'brachydactyly',\n",
       " 'bracingly',\n",
       " 'braggartly',\n",
       " 'braggingly',\n",
       " 'braggishly',\n",
       " 'brainlessly',\n",
       " 'brainsickly',\n",
       " 'brambly',\n",
       " 'brassily',\n",
       " 'bravely',\n",
       " 'brawlingly',\n",
       " 'brawly',\n",
       " 'brawnily',\n",
       " 'brazenfacedly',\n",
       " 'brazenly',\n",
       " 'breakably',\n",
       " 'breathingly',\n",
       " 'breathlessly',\n",
       " 'breezily',\n",
       " 'bremely',\n",
       " 'brickly',\n",
       " 'bridally',\n",
       " 'bridely',\n",
       " 'brieflessly',\n",
       " 'briefly',\n",
       " 'brigandishly',\n",
       " 'brightly',\n",
       " 'brilliantly',\n",
       " 'brimfully',\n",
       " 'brimmingly',\n",
       " 'briskly',\n",
       " 'bristly',\n",
       " 'Britannically',\n",
       " 'Britishly',\n",
       " 'brittlely',\n",
       " 'broadly',\n",
       " 'broilingly',\n",
       " 'brokenheartedly',\n",
       " 'brokenly',\n",
       " 'brolly',\n",
       " 'bromidically',\n",
       " 'bromometrically',\n",
       " 'bronchially',\n",
       " 'broodingly',\n",
       " 'brotherly',\n",
       " 'brownly',\n",
       " 'brusquely',\n",
       " 'brutally',\n",
       " 'brutely',\n",
       " 'brutishly',\n",
       " 'bubblingly',\n",
       " 'bubbly',\n",
       " 'buccally',\n",
       " 'buckishly',\n",
       " 'bucolically',\n",
       " 'buirdly',\n",
       " 'bulkily',\n",
       " 'bullheadedly',\n",
       " 'bullishly',\n",
       " 'bully',\n",
       " 'bumpily',\n",
       " 'bumpingly',\n",
       " 'bumpkinly',\n",
       " 'bumptiously',\n",
       " 'bunchily',\n",
       " 'bungerly',\n",
       " 'bunglingly',\n",
       " 'buoyantly',\n",
       " 'burbly',\n",
       " 'burdensomely',\n",
       " 'bureaucratically',\n",
       " 'burglariously',\n",
       " 'burlesquely',\n",
       " 'burlily',\n",
       " 'burly',\n",
       " 'burningly',\n",
       " 'bushily',\n",
       " 'busily',\n",
       " 'bustlingly',\n",
       " 'butcherly',\n",
       " 'butterfly',\n",
       " 'butyrically',\n",
       " 'buxomly',\n",
       " 'buzzardly',\n",
       " 'buzzingly',\n",
       " 'byously',\n",
       " 'Byronically',\n",
       " 'cabalistically',\n",
       " 'cacophonically',\n",
       " 'cacophonously',\n",
       " ...]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_letters = defaultdict(list)\n",
    "words = nltk.corpus.words.words('en')\n",
    "for word in words:\n",
    "   key = word[-2:]\n",
    "   last_letters[key].append(word)\n",
    "\n",
    "last_letters['ly']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b52e6d23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['blazy',\n",
       " 'bleezy',\n",
       " 'blowzy',\n",
       " 'boozy',\n",
       " 'breezy',\n",
       " 'bronzy',\n",
       " 'buzzy',\n",
       " 'Chazy',\n",
       " 'cozy',\n",
       " 'crazy',\n",
       " 'dazy',\n",
       " 'dizzy',\n",
       " 'dozy',\n",
       " 'enfrenzy',\n",
       " 'fezzy',\n",
       " 'fizzy',\n",
       " 'floozy',\n",
       " 'fozy',\n",
       " 'franzy',\n",
       " 'frenzy',\n",
       " 'friezy',\n",
       " 'frizzy',\n",
       " 'frowzy',\n",
       " 'furzy',\n",
       " 'fuzzy',\n",
       " 'gauzy',\n",
       " 'gazy',\n",
       " 'glazy',\n",
       " 'groszy',\n",
       " 'hazy',\n",
       " 'heezy',\n",
       " 'Izzy',\n",
       " 'jazzy',\n",
       " 'Jozy',\n",
       " 'lawzy',\n",
       " 'lazy',\n",
       " 'mazy',\n",
       " 'mizzy',\n",
       " 'muzzy',\n",
       " 'nizy',\n",
       " 'oozy',\n",
       " 'quartzy',\n",
       " 'quizzy',\n",
       " 'refrenzy',\n",
       " 'ritzy',\n",
       " 'Shortzy',\n",
       " 'sizy',\n",
       " 'sleazy',\n",
       " 'sneezy',\n",
       " 'snoozy',\n",
       " 'squeezy',\n",
       " 'Suzy',\n",
       " 'tanzy',\n",
       " 'tizzy',\n",
       " 'topazy',\n",
       " 'trotcozy',\n",
       " 'twazzy',\n",
       " 'unbreezy',\n",
       " 'unfrizzy',\n",
       " 'wheezy',\n",
       " 'woozy',\n",
       " 'wuzzy',\n",
       " 'yezzy']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_letters['zy']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94fbcbb6",
   "metadata": {},
   "source": [
    "下面的例子使用相同的模式创建一个颠倒顺序的词字典。（你可能会试验第3行来弄清楚为什么这个程序能运行。）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bd965437",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['entrail', 'latrine', 'ratline', 'reliant', 'retinal', 'trenail']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anagrams = defaultdict(list)\n",
    "for word in words:\n",
    "   key = ''.join(sorted(word))\n",
    "   anagrams[key].append(word)\n",
    "\n",
    "anagrams['aeilnrt']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e419cb8f",
   "metadata": {},
   "source": [
    "由于积累这样的词是如此常用的任务，NLTK提供一个创建`defaultdict(list)`更方便的方式，形式为`nltk.Index()`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c93cd1d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['entrail', 'latrine', 'ratline', 'reliant', 'retinal', 'trenail']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anagrams = nltk.Index((''.join(sorted(w)), w) for w in words)\n",
    "anagrams['aeilnrt']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b039980",
   "metadata": {},
   "source": [
    "注意\n",
    "\n",
    "`nltk.Index`是一个支持额外初始化的`defaultdict(list)`。类似地，`nltk.FreqDist`本质上是一个额外支持初始化的`defaultdict(int)`（附带排序和绘图方法）。\n",
    "\n",
    "## 3.6 复杂的键和值\n",
    "\n",
    "我们可以使用具有复杂的键和值的默认字典。让我们研究一个词可能的标记的范围，给定词本身和它前一个词的标记。我们将看到这些信息如何被一个词性标注器使用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "94c5f232",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(int, {'NOUN': 5, 'ADJ': 11})"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos = defaultdict(lambda: defaultdict(int))\n",
    "brown_news_tagged = brown.tagged_words(categories='news', tagset='universal')\n",
    "for ((w1, t1), (w2, t2)) in nltk.bigrams(brown_news_tagged): \n",
    "   pos[(t1, w2)][t2] += 1 \n",
    "\n",
    "pos[('DET', 'right')] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79870beb",
   "metadata": {},
   "source": [
    "这个例子使用一个字典，它的条目的默认值也是一个字典（其默认值是`int()`，即0）。请注意我们如何遍历已标注语料库的双连词，每次遍历处理一个词-标记对 [# 1](https://usyiyi.github.io/nlp-py-2e-zh/5.html#processing-pairs)。每次通过循环时，我们更新字典`pos`中的条目`(t1, w2)`，一个标记和它*后面*的词 [# 2](https://usyiyi.github.io/nlp-py-2e-zh/5.html#tag-word-update)。当我们在`pos`中查找一个项目时，我们必须指定一个复合键 [# 3](https://usyiyi.github.io/nlp-py-2e-zh/5.html#compound-key)，然后得到一个字典对象。一个词性标注器可以使用这些信息来决定词right，前面是一个限定词时，应标注为`ADJ`。\n",
    "\n",
    "## 3.7 反转字典\n",
    "\n",
    "字典支持高效查找，只要你想获得任意键的值。如果`d`是一个字典，`k`是一个键，输入`d[k]`，就立即获得值。给定一个值查找对应的键要慢一些和麻烦一些："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7745836a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mortal',\n",
       " 'Against',\n",
       " 'Him',\n",
       " 'There',\n",
       " 'brought',\n",
       " 'King',\n",
       " 'virtue',\n",
       " 'every',\n",
       " 'been',\n",
       " 'thine']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = defaultdict(int)\n",
    "for word in nltk.corpus.gutenberg.words('milton-paradise.txt'):\n",
    "   counts[word] += 1\n",
    "\n",
    "[key for (key, value) in counts.items() if value == 32]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "537a8210",
   "metadata": {},
   "source": [
    "如果我们希望经常做这样的一种“反向查找”，建立一个映射值到键的字典是有用的。在没有两个键具有相同的值情况，这是一个容易的事。只要得到字典中的所有键-值对，并创建一个新的值-键对字典。下一个例子演示了用键-值对初始化字典`pos`的另一种方式。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5ee81c23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ideas'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos = {'colorless': 'ADJ', 'ideas': 'N', 'sleep': 'V', 'furiously': 'ADV'}\n",
    "pos2 = dict((value, key) for (key, value) in pos.items())\n",
    "pos2['N']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f72a3f7",
   "metadata": {},
   "source": [
    "首先让我们将我们的词性字典做的更实用些，使用字典的`update()`方法加入再一些词到`pos`中，创建多个键具有相同的值的情况。这样一来，刚才看到的反向查找技术就将不再起作用（为什么不？）作为替代，我们不得不使用`append()`积累词和每个词性，如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cf448caa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['furiously', 'peacefully']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos.update({'cats': 'N', 'scratch': 'V', 'peacefully': 'ADV', 'old': 'ADJ'})\n",
    "pos2 = defaultdict(list)\n",
    "for key, value in pos.items():\n",
    "   pos2[value].append(key)\n",
    "\n",
    "pos2['ADV']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8915e031",
   "metadata": {},
   "source": [
    "现在，我们已经反转字典`pos`，可以查任意词性找到所有具有此词性的词。可以使用NLTK中的索引支持更容易的做同样的事，如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "10d29325",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['furiously', 'peacefully']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos2 = nltk.Index((value, key) for (key, value) in pos.items())\n",
    "pos2['ADV']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7f6ac85",
   "metadata": {},
   "source": [
    "[3.2](https://usyiyi.github.io/nlp-py-2e-zh/5.html#tab-dict)给出Python字典方法的总结。\n",
    "\n",
    "表 3.2：\n",
    "\n",
    "Python字典方法：常用的方法与字典相关习惯用法的总结。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3a5b59e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import brown\n",
    "brown_tagged_sents = brown.tagged_sents(categories='news')\n",
    "brown_sents = brown.sents(categories='news')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "59cb91fe",
   "metadata": {},
   "source": [
    "## 4.1 默认标注器\n",
    "\n",
    "\n",
    "\n",
    "<a href=\"#41-默认标注器\">4.1 默认标注器</a>\n",
    "\n",
    "<a href=\"#42-正则表达式标注器\">4.2 正则表达式标注器</a>\n",
    "\n",
    "<a href=\"#43-查询标注器\">4.3 查询标注器</a>\n",
    "\n",
    "<a href=\"#44-评估\">4.4 评估</a>\n",
    "\n",
    "\n",
    "\n",
    "最简单的标注器是为每个词符分配同样的标记。这似乎是一个相当平庸的一步，但它建立了标注器性能的一个重要的底线。为了得到最好的效果，我们用最有可能的标记标注每个词。让我们找出哪个标记是最有可能的（现在使用未简化标记集）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ec1a8553",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NN'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags = [tag for (word, tag) in brown.tagged_words(categories='news')]\n",
    "nltk.FreqDist(tags).max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad053754",
   "metadata": {},
   "source": [
    "现在我们可以创建一个将所有词都标注成`NN`的标注器。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ad77771a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('I', 'NN'),\n",
       " ('do', 'NN'),\n",
       " ('not', 'NN'),\n",
       " ('like', 'NN'),\n",
       " ('green', 'NN'),\n",
       " ('eggs', 'NN'),\n",
       " ('and', 'NN'),\n",
       " ('ham', 'NN'),\n",
       " (',', 'NN'),\n",
       " ('I', 'NN'),\n",
       " ('do', 'NN'),\n",
       " ('not', 'NN'),\n",
       " ('like', 'NN'),\n",
       " ('them', 'NN'),\n",
       " ('Sam', 'NN'),\n",
       " ('I', 'NN'),\n",
       " ('am', 'NN'),\n",
       " ('!', 'NN')]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk import word_tokenize # 代码中断后的重新导入\n",
    "\n",
    "raw = 'I do not like green eggs and ham, I do not like them Sam I am!'\n",
    "tokens = word_tokenize(raw)\n",
    "default_tagger = nltk.DefaultTagger('NN')\n",
    "default_tagger.tag(tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5933d354",
   "metadata": {},
   "source": [
    "不出所料，这种方法的表现相当不好。在一个典型的语料库中，它只标注正确了八分之一的标识符，正如我们在这里看到的："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "73681bd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13089484257215028"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.metrics import accuracy\n",
    "\n",
    "default_tagger.accuracy(brown_tagged_sents)\n",
    "# default_tagger.evaluate(brown_tagged_sents) # 原代码 evaluate() 方法已弃用，更改为 accuracy() 方法"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15e856f3",
   "metadata": {},
   "source": [
    "默认的标注器给每一个单独的词分配标记，即使是之前从未遇到过的词。碰巧的是，一旦我们处理了几千词的英文文本之后，大多数新词都将是名词。正如我们将看到的，这意味着，默认标注器可以帮助我们提高语言处理系统的稳定性。我们将很快回来讲述这个。\n",
    "\n",
    "## 4.2 正则表达式标注器\n",
    "\n",
    "正则表达式标注器基于匹配模式分配标记给词符。例如，我们可能会猜测任一以ed结尾的词都是动词过去分词，任一以's结尾的词都是名词所有格。可以用一个正则表达式的列表表示这些："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "34ca6ea5",
   "metadata": {},
   "outputs": [],
   "source": [
    "patterns = [\n",
    "   (r'.*ing$', 'VBG'),               # gerunds\n",
    "   (r'.*ed$', 'VBD'),                # simple past\n",
    "   (r'.*es$', 'VBZ'),                # 3rd singular present\n",
    "   (r'.*ould$', 'MD'),               # modals\n",
    "   (r'.*\\'s$', 'NN$'),               # possessive nouns\n",
    "   (r'.*s$', 'NNS'),                 # plural nouns\n",
    "   (r'^-?[0-9]+(.[0-9]+)?$', 'CD'),  # cardinal numbers\n",
    "   (r'.*', 'NN')                     # nouns (default)\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a095af18",
   "metadata": {},
   "source": [
    "请注意，这些是顺序处理的，第一个匹配上的会被使用。现在我们可以建立一个标注器，并用它来标记一个句子。做完这一步会有约五分之一是正确的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "793b7ef2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('``', 'NN'),\n",
       " ('Only', 'NN'),\n",
       " ('a', 'NN'),\n",
       " ('relative', 'NN'),\n",
       " ('handful', 'NN'),\n",
       " ('of', 'NN'),\n",
       " ('such', 'NN'),\n",
       " ('reports', 'NNS'),\n",
       " ('was', 'NNS'),\n",
       " ('received', 'VBD'),\n",
       " (\"''\", 'NN'),\n",
       " (',', 'NN'),\n",
       " ('the', 'NN'),\n",
       " ('jury', 'NN'),\n",
       " ('said', 'NN'),\n",
       " (',', 'NN'),\n",
       " ('``', 'NN'),\n",
       " ('considering', 'VBG'),\n",
       " ('the', 'NN'),\n",
       " ('widespread', 'NN'),\n",
       " ('interest', 'NN'),\n",
       " ('in', 'NN'),\n",
       " ('the', 'NN'),\n",
       " ('election', 'NN'),\n",
       " (',', 'NN'),\n",
       " ('the', 'NN'),\n",
       " ('number', 'NN'),\n",
       " ('of', 'NN'),\n",
       " ('voters', 'NNS'),\n",
       " ('and', 'NN'),\n",
       " ('the', 'NN'),\n",
       " ('size', 'NN'),\n",
       " ('of', 'NN'),\n",
       " ('this', 'NNS'),\n",
       " ('city', 'NN'),\n",
       " (\"''\", 'NN'),\n",
       " ('.', 'NN')]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regexp_tagger = nltk.RegexpTagger(patterns)\n",
    "regexp_tagger.tag(brown_sents[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d42ec096",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20326391789486245"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# regexp_tagger.evaluate(brown_tagged_sents)\n",
    "regexp_tagger.accuracy(brown_tagged_sents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3e447c3",
   "metadata": {},
   "source": [
    "最终的正则表达式«`.*`»是一个全面捕捉的，标注所有词为名词。这与默认标注器是等效的（只是效率低得多）。除了作为正则表达式标注器的一部分重新指定这个，有没有办法结合这个标注器和默认标注器呢？我们将很快看到如何做到这一点。\n",
    "\n",
    "注意\n",
    "\n",
    "**轮到你来：**看看你能不能想出一些模式，提高上面所示的正则表达式标注器的性能。（请注意 [1](https://usyiyi.github.io/nlp-py-2e-zh/6.html#sec-supervised-classification)描述部分自动化这类工作的方法。）\n",
    "\n",
    "## 4.3 查询标注器\n",
    "\n",
    "很多高频词没有`NN`标记。让我们找出100个最频繁的词，存储它们最有可能的标记。然后我们可以使用这个信息作为“查找标注器”（NLTK `UnigramTagger`）的模型："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "2bd4bfef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.45578495136941344"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fd = nltk.FreqDist(brown.words(categories='news'))\n",
    "cfd = nltk.ConditionalFreqDist(brown.tagged_words(categories='news'))\n",
    "most_freq_words = fd.most_common(100)\n",
    "likely_tags = dict((word, cfd[word].max()) for (word, _) in most_freq_words)\n",
    "baseline_tagger = nltk.UnigramTagger(model=likely_tags)\n",
    "# baseline_tagger.evaluate(brown_tagged_sents)\n",
    "baseline_tagger.accuracy(brown_tagged_sents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9501bff3",
   "metadata": {},
   "source": [
    "现在应该并不奇怪，仅仅知道100个最频繁的词的标记就使我们能正确标注很大一部分词符（近一半，事实上）。让我们来看看它在一些未标注的输入文本上做的如何："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "fd8a624c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('``', '``'),\n",
       " ('Only', None),\n",
       " ('a', 'AT'),\n",
       " ('relative', None),\n",
       " ('handful', None),\n",
       " ('of', 'IN'),\n",
       " ('such', None),\n",
       " ('reports', None),\n",
       " ('was', 'BEDZ'),\n",
       " ('received', None),\n",
       " (\"''\", \"''\"),\n",
       " (',', ','),\n",
       " ('the', 'AT'),\n",
       " ('jury', None),\n",
       " ('said', 'VBD'),\n",
       " (',', ','),\n",
       " ('``', '``'),\n",
       " ('considering', None),\n",
       " ('the', 'AT'),\n",
       " ('widespread', None),\n",
       " ('interest', None),\n",
       " ('in', 'IN'),\n",
       " ('the', 'AT'),\n",
       " ('election', None),\n",
       " (',', ','),\n",
       " ('the', 'AT'),\n",
       " ('number', None),\n",
       " ('of', 'IN'),\n",
       " ('voters', None),\n",
       " ('and', 'CC'),\n",
       " ('the', 'AT'),\n",
       " ('size', None),\n",
       " ('of', 'IN'),\n",
       " ('this', 'DT'),\n",
       " ('city', None),\n",
       " (\"''\", \"''\"),\n",
       " ('.', '.')]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent = brown.sents(categories='news')[3]\n",
    "baseline_tagger.tag(sent)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c9bbb11",
   "metadata": {},
   "source": [
    "许多词都被分配了一个`None`标签，因为它们不在100个最频繁的词之中。在这些情况下，我们想分配默认标记`NN`。换句话说，我们要先使用查找表，如果它不能指定一个标记就使用默认标注器，这个过程叫做回退（[5](https://usyiyi.github.io/nlp-py-2e-zh/5.html#sec-n-gram-tagging)）。我们可以做到这个，通过指定一个标注器作为另一个标注器的参数，如下所示。现在查找标注器将只存储名词以外的词的词-标记对，只要它不能给一个词分配标记，它将会调用默认标注器。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "cc0ec306",
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline_tagger = nltk.UnigramTagger(model=likely_tags, backoff=nltk.DefaultTagger('NN'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29664c6c",
   "metadata": {},
   "source": [
    "让我们把所有这些放在一起，写一个程序来创建和评估具有一定范围的查找标注器 ，[4.1](https://usyiyi.github.io/nlp-py-2e-zh/5.html#code-baseline-tagger)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "bbc23278",
   "metadata": {},
   "outputs": [],
   "source": [
    "def performance(cfd, wordlist):\n",
    "    lt = dict((word, cfd[word].max()) for word in wordlist)\n",
    "    baseline_tagger = nltk.UnigramTagger(model=lt, backoff=nltk.DefaultTagger('NN'))\n",
    "    return baseline_tagger.evaluate(brown.tagged_sents(categories='news'))\n",
    "\n",
    "def display():\n",
    "    import pylab\n",
    "    word_freqs = nltk.FreqDist(brown.words(categories='news')).most_common()\n",
    "    words_by_freq = [w for (w, _) in word_freqs]\n",
    "    cfd = nltk.ConditionalFreqDist(brown.tagged_words(categories='news'))\n",
    "    sizes = 2 ** pylab.arange(15)\n",
    "    perfs = [performance(cfd, words_by_freq[:size]) for size in sizes]\n",
    "    pylab.plot(sizes, perfs, '-bo')\n",
    "    pylab.title('Lookup Tagger Performance with Varying Model Size')\n",
    "    pylab.xlabel('Model Size')\n",
    "    pylab.ylabel('Performance')\n",
    "    pylab.show()"
   ]
  },
  {
   "attachments": {
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    }
   },
   "cell_type": "markdown",
   "id": "2292f291",
   "metadata": {},
   "source": [
    "![f093aaace735b4961dbf9fa7d5c8ca37.png](attachment:f093aaace735b4961dbf9fa7d5c8ca37.png)\n",
    "\n",
    "图 4.2：查找标注器\n",
    "\n",
    "可以观察到，随着模型规模的增长，最初的性能增加迅速，最终达到一个稳定水平，这时模型的规模大量增加性能的提高很小。（这个例子使用`pylab`绘图软件包，在[4.8](https://usyiyi.github.io/nlp-py-2e-zh/4.html#sec-libraries)讨论过）。\n",
    "\n",
    "## 4.4 评估\n",
    "\n",
    "在前面的例子中，你会注意到对准确性得分的强调。事实上，评估这些工具的表现是NLP的一个中心主题。回想[fig-sds](https://usyiyi.github.io/nlp-py-2e-zh/1.html#fig-sds)中的处理流程；一个模块输出中的任何错误都在下游模块大大的放大。\n",
    "\n",
    "我们对比人类专家分配的标记来评估一个标注器的表现。由于我们通常很难获得专业和公正的人的判断，所以使用黄金标准测试数据来代替。这是一个已经手动标注并作为自动系统评估标准而被接受的语料库。当标注器对给定词猜测的标记与黄金标准标记相同，标注器被视为是正确的。\n",
    "\n",
    "当然，设计和实施原始的黄金标准标注的也是人。更深入的分析可能会显示黄金标准中的错误，或者可能最终会导致一个修正的标记集和更复杂的指导方针。然而，黄金标准就目前有关的自动标注器的评估而言被定义成“正确的”。\n",
    "\n",
    "注意\n",
    "\n",
    "开发一个已标注语料库是一个重大的任务。除了数据，它会产生复杂的工具、文档和实践，为确保高品质的标注。标记集和其他编码方案不可避免地依赖于一些理论主张，不是所有的理论主张都被共享，然而，语料库的创作者往往竭尽全力使他们的工作尽可能理论中立，以最大限度地提高其工作的有效性。我们将在[11.](https://usyiyi.github.io/nlp-py-2e-zh/ch11.html#chap-data)讨论创建一个语料库的挑战。\n",
    "\n",
    "## 5 N-gram标注\n",
    "\n",
    "\n",
    "\n",
    "<a href=\"#51-—元标注\">5.1 —元标注</a>\n",
    "\n",
    "<a href=\"#52-分离训练和测试数据\">5.2 分离训练和测试数据</a>\n",
    "\n",
    "<a href=\"#53-一般的n-gram标注\">5.3 —般的N-gram标注</a>\n",
    "\n",
    "<a href=\"#54-组合标注器\">5.4 组合标注器</a>\n",
    "\n",
    "<a href=\"#55-标注生词\">5.5 标注生词</a>\n",
    "\n",
    "<a href=\"#56-存储标注器\">5.6 存储标注器</a>\n",
    "\n",
    "<a href=\"#57-准确性的极限\">5.7 准确性的极限</a>\n",
    "\n",
    "\n",
    "\n",
    "## 5.1 一元标注\n",
    "\n",
    "一元标注器基于一个简单的统计算法：对每个标识符分配这个独特的标识符最有可能的标记。例如，它将分配标记`JJ`给词frequent的所有出现，因为frequent用作一个形容词（例如a frequent word）比用作一个动词（例如I frequent this cafe）更常见。一个一元标注器的行为就像一个查找标注器（[4](https://usyiyi.github.io/nlp-py-2e-zh/5.html#sec-automatic-tagging)），除了有一个更方便的建立它的技术，称为训练。在下面的代码例子中，我们训练一个一元标注器，用它来标注一个句子，然后评估："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "db3ae3e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Various', 'JJ'),\n",
       " ('of', 'IN'),\n",
       " ('the', 'AT'),\n",
       " ('apartments', 'NNS'),\n",
       " ('are', 'BER'),\n",
       " ('of', 'IN'),\n",
       " ('the', 'AT'),\n",
       " ('terrace', 'NN'),\n",
       " ('type', 'NN'),\n",
       " (',', ','),\n",
       " ('being', 'BEG'),\n",
       " ('on', 'IN'),\n",
       " ('the', 'AT'),\n",
       " ('ground', 'NN'),\n",
       " ('floor', 'NN'),\n",
       " ('so', 'QL'),\n",
       " ('that', 'CS'),\n",
       " ('entrance', 'NN'),\n",
       " ('is', 'BEZ'),\n",
       " ('direct', 'JJ'),\n",
       " ('.', '.')]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.corpus import brown\n",
    "brown_tagged_sents = brown.tagged_sents(categories='news')\n",
    "brown_sents = brown.sents(categories='news')\n",
    "unigram_tagger = nltk.UnigramTagger(brown_tagged_sents)\n",
    "unigram_tagger.tag(brown_sents[2007])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7013b44b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9349006503968017"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# unigram_tagger.evaluate(brown_tagged_sents)\n",
    "unigram_tagger.accuracy(brown_tagged_sents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3f5861d",
   "metadata": {},
   "source": [
    "我们训练一个`UnigramTagger`，通过在我们初始化标注器时指定已标注的句子数据作为参数。训练过程中涉及检查每个词的标记，将所有词的最可能的标记存储在一个字典里面，这个字典存储在标注器内部。\n",
    "\n",
    "## 5.2 分离训练和测试数据\n",
    "\n",
    "现在，我们正在一些数据上训练一个标注器，必须小心不要在相同的数据上测试，如我们在前面的例子中的那样。一个只是记忆它的训练数据，而不试图建立一个一般的模型的标注器会得到一个完美的得分，但在标注新的文本时将是无用的。相反，我们应该分割数据，90％为测试数据，其余10％为测试数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6becddd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4160"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size = int(len(brown_tagged_sents) * 0.9)\n",
    "size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6c2fd5e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8121200039868434"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_sents = brown_tagged_sents[:size]\n",
    "test_sents = brown_tagged_sents[size:]\n",
    "unigram_tagger = nltk.UnigramTagger(train_sents)\n",
    "# unigram_tagger.evaluate(test_sents)\n",
    "unigram_tagger.accuracy(test_sents)"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "57c541a4",
   "metadata": {},
   "source": [
    "虽然得分更糟糕了，但是现在我们对这种标注器的用处有了更好的了解，如它在之前没有遇见的文本上的表现。\n",
    "\n",
    "## 5.3 一般的N-gram标注\n",
    "\n",
    "在基于一元处理一个语言处理任务时，我们使用上下文中的一个项目。标注的时候，我们只考虑当前的词符，与更大的上下文隔离。给定一个模型，我们能做的最好的是为每个词标注其*先验的*最可能的标记。这意味着我们将使用相同的标记标注一个词，如wind，不论它出现的上下文是the wind还是to wind。\n",
    "\n",
    "一个n-gram tagger标注器是一个一元标注器的一般化，它的上下文是当前词和它前面*n*-1个标识符的词性标记，如图[5.1](https://usyiyi.github.io/nlp-py-2e-zh/5.html#fig-tag-context)所示。要选择的标记是圆圈里的*t*n，灰色阴影的是上下文。在[5.1](https://usyiyi.github.io/nlp-py-2e-zh/5.html#fig-tag-context)所示的n-gram标注器的例子中，我们让*n*=3；也就是说，我们考虑当前词的前两个词的标记。一个n-gram标注器挑选在给定的上下文中最有可能的标记。\n",
    "\n",
    "![12573c3a9015654728fe798e170a3c50.png](attachment:12573c3a9015654728fe798e170a3c50.png))\n",
    "\n",
    "图 5.1：标注器上下文\n",
    "\n",
    "注意\n",
    "\n",
    "1-gram标注器是一元标注器另一个名称：即用于标注一个词符的上下文的只是词符本身。2-gram标注器也称为*二元标注器*，3-gram标注器也称为*三元标注器*。\n",
    "\n",
    "`NgramTagger`类使用一个已标注的训练语料库来确定对每个上下文哪个词性标记最有可能。这里我们看n-gram标注器的一个特殊情况，二元标注器。首先，我们训练它，然后用它来标注未标注的句子："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "dd1eb4b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Various', 'JJ'),\n",
       " ('of', 'IN'),\n",
       " ('the', 'AT'),\n",
       " ('apartments', 'NNS'),\n",
       " ('are', 'BER'),\n",
       " ('of', 'IN'),\n",
       " ('the', 'AT'),\n",
       " ('terrace', 'NN'),\n",
       " ('type', 'NN'),\n",
       " (',', ','),\n",
       " ('being', 'BEG'),\n",
       " ('on', 'IN'),\n",
       " ('the', 'AT'),\n",
       " ('ground', 'NN'),\n",
       " ('floor', 'NN'),\n",
       " ('so', 'CS'),\n",
       " ('that', 'CS'),\n",
       " ('entrance', 'NN'),\n",
       " ('is', 'BEZ'),\n",
       " ('direct', 'JJ'),\n",
       " ('.', '.')]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigram_tagger = nltk.BigramTagger(train_sents)\n",
    "bigram_tagger.tag(brown_sents[2007])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6161c084",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('The', 'AT'),\n",
       " ('population', 'NN'),\n",
       " ('of', 'IN'),\n",
       " ('the', 'AT'),\n",
       " ('Congo', 'NP'),\n",
       " ('is', 'BEZ'),\n",
       " ('13.5', None),\n",
       " ('million', None),\n",
       " (',', None),\n",
       " ('divided', None),\n",
       " ('into', None),\n",
       " ('at', None),\n",
       " ('least', None),\n",
       " ('seven', None),\n",
       " ('major', None),\n",
       " ('``', None),\n",
       " ('culture', None),\n",
       " ('clusters', None),\n",
       " (\"''\", None),\n",
       " ('and', None),\n",
       " ('innumerable', None),\n",
       " ('tribes', None),\n",
       " ('speaking', None),\n",
       " ('400', None),\n",
       " ('separate', None),\n",
       " ('dialects', None),\n",
       " ('.', None)]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unseen_sent = brown_sents[4203]\n",
    "bigram_tagger.tag(unseen_sent)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08585fde",
   "metadata": {},
   "source": [
    "请注意，二元标注器能够标注训练中它看到过的句子中的所有词，但对一个没见过的句子表现很差。只要遇到一个新词（如13.5），就无法给它分配标记。它不能标注下面的词（如million)，即使是在训练过程中看到过的，只是因为在训练过程中从来没有见过它前面有一个`None`标记的词。因此，标注器标注句子的其余部分也失败了。它的整体准确度得分非常低："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "ad9eac7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10206319146815508"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bigram_tagger.evaluate(test_sents)\n",
    "bigram_tagger.accuracy(test_sents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "525c91d3",
   "metadata": {},
   "source": [
    "当*n*越大，上下文的特异性就会增加，我们要标注的数据中包含训练数据中不存在的上下文的几率也增大。这被称为*数据稀疏*问题，在NLP中是相当普遍的。因此，我们的研究结果的精度和覆盖范围之间需要有一个权衡（这与信息检索中的精度/召回权衡有关）。\n",
    "\n",
    "小心！\n",
    "\n",
    "N-gram标注器不应考虑跨越句子边界的上下文。因此，NLTK的标注器被设计用于句子列表，其中一个句子是一个词列表。在一个句子的开始，*t*n-1和前面的标记被设置为`None`。\n",
    "\n",
    "## 5.4 组合标注器\n",
    "\n",
    "解决精度和覆盖范围之间的权衡的一个办法是尽可能的使用更精确的算法，但却在很多时候落后于具有更广覆盖范围的算法。例如，我们可以按如下方式组合二元标注器、一元注器和一个默认标注器，如下：\n",
    "\n",
    "1. 尝试使用二元标注器标注标识符。\n",
    "2. 如果二元标注器无法找到一个标记，尝试一元标注器。\n",
    "3. 如果一元标注器也无法找到一个标记，使用默认标注器。\n",
    "\n",
    "大多数NLTK标注器允许指定一个回退标注器。回退标注器自身可能也有一个回退标注器："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "41e6112b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8452108043456593"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t0 = nltk.DefaultTagger('NN')\n",
    "t1 = nltk.UnigramTagger(train_sents, backoff=t0)\n",
    "t2 = nltk.BigramTagger(train_sents, backoff=t1)\n",
    "# t2.evaluate(test_sents)\n",
    "t2.accuracy(test_sents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5c74f14",
   "metadata": {},
   "source": [
    "注意\n",
    "\n",
    "**轮到你来：** 通过定义一个名为`t3`的`TrigramTagger`，扩展前面的例子，它是`t2`的回退标注器。\n",
    "\n",
    "请注意，我们在标注器初始化时指定回退标注器，从而使训练能利用回退标注器。于是，在一个特定的上下文中，如果二元标注器将分配与它的一元回退标注器一样的标记，那么二元标注器丢弃训练的实例。这样保持尽可能小的二元标注器模型。我们可以进一步指定一个标注器需要看到一个上下文的多个实例才能保留它，例如`nltk.BigramTagger(sents, cutoff=2, backoff=t1)`将会丢弃那些只看到一次或两次的上下文。\n",
    "\n",
    "## 5.5 标注生词\n",
    "\n",
    "我们标注生词的方法仍然是回退到一个正则表达式标注器或一个默认标注器。这些都无法利用上下文。因此，如果我们的标注器遇到词blog，训练过程中没有看到过，它会分配相同的标记，不论这个词出现的上下文是the blog还是to blog。我们怎样才能更好地处理这些生词，或词汇表以外的项目？\n",
    "\n",
    "一个有用的基于上下文标注生词的方法是限制一个标注器的词汇表为最频繁的n 个词，使用 [3](https://usyiyi.github.io/nlp-py-2e-zh/5.html#sec-dictionaries)中的方法替代每个其他的词为一个特殊的词UNK。训练时，一个一元标注器可能会学到UNK通常是一个名词。然而，n-gram标注器会检测它的一些其他标记中的上下文。例如，如果前面的词是to（标注为`TO`），那么UNK可能会被标注为一个动词。\n",
    "\n",
    "## 5.6 存储标注器\n",
    "\n",
    "在大语料库上训练一个标注器可能需要大量的时间。没有必要在每次我们需要的时候训练一个标注器，很容易将一个训练好的标注器保存到一个文件以后重复使用。让我们保存我们的标注器`t2`到文件`t2.pkl`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "daf1578b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pickle import dump\n",
    "output = open('t2.pkl', 'wb')\n",
    "dump(t2, output, -1)\n",
    "output.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c8db1b3",
   "metadata": {},
   "source": [
    "现在，我们可以在一个单独的Python进程中，我们可以载入保存的标注器。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "dcdeaac4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pickle import load\n",
    "input = open('t2.pkl', 'rb')\n",
    "tagger = load(input)\n",
    "input.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13a2dbf5",
   "metadata": {},
   "source": [
    "现在让我们检查它是否可以用来标注。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "b4e7314e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('The', 'AT'),\n",
       " (\"board's\", 'NN$'),\n",
       " ('action', 'NN'),\n",
       " ('shows', 'NNS'),\n",
       " ('what', 'WDT'),\n",
       " ('free', 'JJ'),\n",
       " ('enterprise', 'NN'),\n",
       " ('is', 'BEZ'),\n",
       " ('up', 'RP'),\n",
       " ('against', 'IN'),\n",
       " ('in', 'IN'),\n",
       " ('our', 'PP$'),\n",
       " ('complex', 'JJ'),\n",
       " ('maze', 'NN'),\n",
       " ('of', 'IN'),\n",
       " ('regulatory', 'NN'),\n",
       " ('laws', 'NNS'),\n",
       " ('.', '.')]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = \"\"\"The board's action shows what free enterprise\n",
    "   is up against in our complex maze of regulatory laws .\"\"\"\n",
    "tokens = text.split()\n",
    "tagger.tag(tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b6b931e",
   "metadata": {},
   "source": [
    "## 5.7 准确性的极限\n",
    "\n",
    "一个n-gram标注器准确性的上限是什么？考虑一个三元标注器的情况。它遇到多少词性歧义的情况？我们可以根据经验决定这个问题的答案："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "115639a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.049297702068029296"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfd = nltk.ConditionalFreqDist(\n",
    "          ((x[1], y[1], z[0]), z[1])\n",
    "          for sent in brown_tagged_sents\n",
    "          for x, y, z in nltk.trigrams(sent))\n",
    "ambiguous_contexts = [c for c in cfd.conditions() if len(cfd[c]) > 1]\n",
    "sum(cfd[c].N() for c in ambiguous_contexts) / cfd.N()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4422e598",
   "metadata": {},
   "source": [
    "因此，1/20的三元是有歧义的[示例]。给定当前单词及其前两个标记，根据训练数据，在5％的情况中，有一个以上的标记可能合理地分配给当前词。假设我们总是挑选在这种含糊不清的上下文中最有可能的标记，可以得出三元标注器准确性的一个下界。\n",
    "\n",
    "调查标注器准确性的另一种方法是研究它的错误。有些标记可能会比别的更难分配，可能需要专门对这些数据进行预处理或后处理。一个方便的方式查看标注错误是混淆矩阵。它用图表表示期望的标记（黄金标准）与实际由标注器产生的标记："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2440582c",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_tags = [tag for sent in brown.sents(categories='editorial')\n",
    "                 for (word, tag) in t2.tag(sent)]\n",
    "gold_tags = [tag for (word, tag) in brown.tagged_words(categories='editorial')]\n",
    "print(nltk.ConfusionMatrix(gold_tags, test_tags))         "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3953282",
   "metadata": {},
   "source": [
    "基于这样的分析，我们可能会决定修改标记集。或许标记之间很难做出的区分可以被丢弃，因为它在一些较大的处理任务的上下文中并不重要。\n",
    "\n",
    "分析标注器准确性界限的另一种方式来自人类标注者之间并非100％的意见一致。[更多]\n",
    "\n",
    "一般情况下，标注过程会损坏区别：例如当所有的人称代词被标注为`PRP`时，词的特性通常会失去。与此同时，标注过程引入了新的区别从而去除了含糊之处：例如deal标注为`VB`或`NN`。这种消除某些区别并引入新的区别的特点是标注的一个重要的特征，有利于分类和预测。当我们引入一个标记集的更细的划分时，在n-gram标注器决定什么样的标记分配给一个特定的词时，可以获得关于左侧上下文的更详细的信息。然而，标注器同时也将需要做更多的工作来划分当前的词符，只是因为有更多可供选择的标记。相反，使用较少的区别（如简化的标记集），标注器有关上下文的信息会减少，为当前词符分类的选择范围也较小。\n",
    "\n",
    "我们已经看到，训练数据中的歧义导致标注器准确性的上限。有时更多的上下文能解决这些歧义。然而，在其他情况下，如[(Church, Young, & Bloothooft, 1996)](https://usyiyi.github.io/nlp-py-2e-zh/bibliography.html#abney1996pst)中指出的，只有参考语法或现实世界的知识，才能解决歧义。尽管有这些缺陷，词性标注在用统计方法进行自然语言处理的兴起过程中起到了核心作用。1990年代初，统计标注器令人惊讶的精度是一个惊人的示范，可以不用更深的语言学知识解决一小部分语言理解问题，即词性消歧。这个想法能再推进吗？第[7.](https://usyiyi.github.io/nlp-py-2e-zh/7.html#chap-chunk)中，我们将看到，它可以。\n",
    "\n",
    "## 6 基于转换的标注\n",
    "\n",
    "n-gram标注器的一个潜在的问题是它们的n-gram表（或语言模型）的大小。如果使用各种语言技术的标注器部署在移动计算设备上，在模型大小和标注器准确性之间取得平衡是很重要的。使用回退标注器的n-gram标注器可能存储trigram和bigram表，这是很大的稀疏阵列，可能有数亿条条目。\n",
    "\n",
    "第二个问题是关于上下文。n-gram标注器从前面的上下文中获得的唯一的信息是标记，虽然词本身可能是一个有用的信息源。n-gram模型使用上下文中的词的其他特征为条件是不切实际的。在本节中，我们考察Brill标注，一种归纳标注方法，它的性能很好，使用的模型只有n-gram标注器的很小一部分。\n",
    "\n",
    "Brill标注是一种*基于转换的学习*，以它的发明者命名。一般的想法很简单：猜每个词的标记，然后返回和修复错误。在这种方式中，Brill标注器陆续将一个不良标注的文本转换成一个更好的。与n-gram标注一样，这是有*监督的学习*方法，因为我们需要已标注的训练数据来评估标注器的猜测是否是一个错误。然而，不像n-gram标注，它不计数观察结果，只编制一个转换修正规则列表。\n",
    "\n",
    "Brill标注的的过程通常是与绘画类比来解释的。假设我们要画一棵树，包括大树枝、树枝、小枝、叶子和一个统一的天蓝色背景的所有细节。不是先画树然后尝试在空白处画蓝色，而是简单的将整个画布画成蓝色，然后通过在蓝色背景上上色“修正”树的部分。以同样的方式，我们可能会画一个统一的褐色的树干再回过头来用更精细的刷子画进一步的细节。Brill标注使用了同样的想法：以大笔画开始，然后修复细节，一点点的细致的改变。让我们看看下面的例子："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "befb3ec7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading tagged data from treebank... \n",
      "Read testing data (200 sents/5251 wds)\n",
      "Read training data (800 sents/19933 wds)\n",
      "Read baseline data (800 sents/19933 wds) [reused the training set]\n",
      "Trained baseline tagger\n",
      "    Accuracy on test set: 0.8358\n",
      "Training tbl tagger...\n",
      "TBL train (fast) (seqs: 800; tokens: 19933; tpls: 24; min score: 3; min acc: None)\n",
      "Finding initial useful rules...\n",
      "    Found 12799 useful rules.\n",
      "\n",
      "           B      |\n",
      "   S   F   r   O  |        Score = Fixed - Broken\n",
      "   c   i   o   t  |  R     Fixed = num tags changed incorrect -> correct\n",
      "   o   x   k   h  |  u     Broken = num tags changed correct -> incorrect\n",
      "   r   e   e   e  |  l     Other = num tags changed incorrect -> incorrect\n",
      "   e   d   n   r  |  e\n",
      "------------------+-------------------------------------------------------\n",
      "  23  23   0   0  | POS->VBZ if Pos:PRP@[-2,-1]\n",
      "  18  19   1   0  | NN->VB if Pos:-NONE-@[-2] & Pos:TO@[-1]\n",
      "  14  14   0   0  | VBP->VB if Pos:MD@[-2,-1]\n",
      "  12  12   0   0  | VBP->VB if Pos:TO@[-1]\n",
      "  11  11   0   0  | VBD->VBN if Pos:VBD@[-1]\n",
      "  11  11   0   0  | IN->WDT if Pos:-NONE-@[1] & Pos:VBP@[2]\n",
      "  10  11   1   0  | VBN->VBD if Pos:PRP@[-1]\n",
      "   9  10   1   0  | VBD->VBN if Pos:VBZ@[-1]\n",
      "   8   8   0   0  | NN->VB if Pos:MD@[-1]\n",
      "   7   7   0   1  | VB->NN if Pos:DT@[-1]\n",
      "   7   7   0   0  | VB->VBP if Pos:PRP@[-1]\n",
      "   7   7   0   0  | IN->WDT if Pos:-NONE-@[1] & Pos:VBZ@[2]\n",
      "   7   8   1   0  | IN->RB if Word:as@[2]\n",
      "   6   6   0   0  | VBD->VBN if Pos:VBP@[-2,-1]\n",
      "   6   6   0   1  | IN->WDT if Pos:-NONE-@[1] & Pos:VBD@[2]\n",
      "   5   5   0   0  | POS->VBZ if Pos:-NONE-@[-1]\n",
      "   5   5   0   0  | VB->VBP if Pos:NNS@[-1]\n",
      "   5   5   0   0  | VBD->VBN if Word:be@[-2,-1]\n",
      "   4   4   0   0  | POS->VBZ if Pos:``@[-2]\n",
      "   4   4   0   0  | VBP->VB if Pos:VBD@[-2,-1]\n",
      "   4   6   2   3  | RP->RB if Pos:CD@[1,2]\n",
      "   4   4   0   0  | RB->JJ if Pos:DT@[-1] & Pos:NN@[1]\n",
      "   4   4   0   0  | NN->VBP if Pos:NNS@[-2] & Pos:RB@[-1]\n",
      "   4   5   1   0  | VBN->VBD if Pos:NNP@[-2] & Pos:NNP@[-1]\n",
      "   4   4   0   0  | IN->WDT if Pos:-NONE-@[1] & Pos:MD@[2]\n",
      "   4   8   4   0  | VBD->VBN if Word:*@[1]\n",
      "   4   4   0   0  | JJS->RBS if Word:most@[0] & Word:the@[-1] & Pos:DT@[-1]\n",
      "   3   3   0   0  | VBD->VBN if Pos:VBN@[-1]\n",
      "   3   4   1   0  | VBN->VB if Pos:TO@[-1]\n",
      "   3   4   1   1  | IN->RB if Pos:.@[1]\n",
      "   3   3   0   0  | JJ->RB if Pos:VBD@[1]\n",
      "   3   3   0   0  | PRP$->PRP if Pos:TO@[1]\n",
      "   3   3   0   0  | NN->VBP if Pos:NNS@[-1] & Pos:DT@[1]\n",
      "   3   3   0   0  | VBP->VB if Word:n't@[-2,-1]\n",
      "Trained tbl tagger in 2.16 seconds\n",
      "    Accuracy on test set: 0.8564\n",
      "Tagging the test data\n"
     ]
    }
   ],
   "source": [
    "from nltk.tbl import demo as brill_tagger\n",
    "brill_tagger.demo()\n",
    "\n",
    "# nltk.tag.brill.demo() # 源代码无效，应导入 nltk.tbl 的 demo\n",
    "# https://github.com/nltk/nltk/issues/1828"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01d6676d",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(open(\"errors.out\").read()) # 如果没有生成 erroes.out 就不用执行"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1264e805",
   "metadata": {},
   "source": [
    "## 7 如何确定一个词的分类\n",
    "\n",
    "我们已经详细研究了词类，现在转向一个更基本的问题：我们如何首先决定一个词属于哪一类？在一般情况下，语言学家使用形态学、句法和语义线索确定一个词的类别。\n",
    "\n",
    "## 7.1 形态学线索\n",
    "\n",
    "一个词的内部结构可能为这个词分类提供有用的线索。举例来说：-ness是一个后缀，与形容词结合产生一个名词，如happy → happiness, ill → illness。如果我们遇到的一个以-ness结尾的词，很可能是一个名词。同样的，-ment是与一些动词结合产生一个名词的后缀，如govern → government和establish → establishment。\n",
    "\n",
    "英语动词也可以是形态复杂的。例如，一个动词的现在分词以-ing结尾，表示正在进行的还没有结束的行动（如falling, eating）。-ing后缀也出现在从动词派生的名词中，如the falling of the leaves（这被称为动名词）。\n",
    "\n",
    "## 7.2 句法线索\n",
    "\n",
    "另一个信息来源是一个词可能出现的典型的上下文语境。例如，假设我们已经确定了名词类。那么我们可以说，英语形容词的句法标准是它可以立即出现在一个名词前，或紧跟在词be或very后。根据这些测试，near应该被归类为形容词："
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a779ad9f",
   "metadata": {},
   "source": [
    "```\n",
    "Statement  User117 Dude, I wanted some of that\n",
    "ynQuestion User120 m I missing something?\n",
    "Bye        User117 I'm gonna go fix food, I'll be back later.\n",
    "System     User122 JOIN\n",
    "System     User2   slaps User122 around a bit with a large trout.\n",
    "Statement  User121 18/m pm me if u tryin to chat\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9544817",
   "metadata": {},
   "source": [
    "## 10 练习\n",
    "\n",
    "1. ☼ 网上搜索“spoof newspaper headlines”，找到这种宝贝：British Left Waffles on Falkland Islands和Juvenile Court to Try Shooting Defendant。手工标注这些头条，看看词性标记的知识是否可以消除歧义。\n",
    "\n",
    "2. ☼ 和别人一起，轮流挑选一个既可以是名词也可以是动词的词（如contest）；让对方预测哪一个可能是布朗语料库中频率最高的；检查对方的预测，为几个回合打分。\n",
    "\n",
    "3. ☼ 分词和标注下面的句子：They wind back the clock, while we chase after the wind。涉及哪些不同的发音和词类？\n",
    "\n",
    "4. ☼ 回顾[3.1](https://usyiyi.github.io/nlp-py-2e-zh/5.html#tab-linguistic-objects)中的映射。讨论你能想到的映射的其他的例子。它们从什么类型的信息映射到什么类型的信息？\n",
    "\n",
    "5. ☼ 在交互模式下使用Python解释器，实验本章中字典的例子。创建一个字典`d`，添加一些条目。如果你尝试访问一个不存在的条目会发生什么，如`d['xyz']`？\n",
    "\n",
    "6. ☼ 尝试从字典`d`删除一个元素，使用语法`del d['abc']`。检查被删除的项目。\n",
    "\n",
    "7. ☼ 创建两个字典，`d1`和`d2`，为每个添加一些条目。现在发出命令`d1.update(d2)`。这做了什么？它可能是有什么用？\n",
    "\n",
    "8. ☼ 创建一个字典`e`，表示你选择的一些词的一个单独的词汇条目。定义键如`headword`、`part-of-speech`、`sense`和`example`，分配给它们适当的值。\n",
    "\n",
    "9. ☼ 自己验证go和went在分布上的限制，也就是说，它们不能自由地在[7](https://usyiyi.github.io/nlp-py-2e-zh/5.html#sec-how-to-determine-the-category-of-a-word)中的[(3d)](https://usyiyi.github.io/nlp-py-2e-zh/5.html#ex-go)演示的那种上下文中互换。\n",
    "\n",
    "10. ☼ 训练一个一元标注器，在一些新的文本上运行。观察有些词没有分配到标记。为什么没有？\n",
    "\n",
    "11. ☼ 了解词缀标注器（输入`help(nltk.AffixTagger)`）。训练一个词缀标注器，在一些新的文本上运行。设置不同的词缀长度和最小词长做实验。讨论你的发现。\n",
    "\n",
    "12. ☼ 训练一个没有回退标注器的二元标注器，在一些训练数据上运行。下一步，在一些新的数据运行它。标注器的准确性会发生什么？为什么呢？\n",
    "\n",
    "13. ☼ 我们可以使用字典指定由一个格式化字符串替换的值。阅读关于格式化字符串的Python库文档`http://docs.python.org/lib/typesseq-strings.html`，使用这种方法以两种不同的格式显示今天的日期。\n",
    "\n",
    "14. ◑ 使用`sorted()`和`set()`获得布朗语料库使用的标记的排序的列表，删除重复。\n",
    "\n",
    "15. ◑ 写程序处理布朗语料库，找到以下问题的答案："
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7c64ad1",
   "metadata": {},
   "source": [
    "1. 哪些名词常以它们复数形式而不是它们的单数形式出现？（只考虑常规的复数形式，-s后缀形式的）。\n",
    "2. 哪个词的不同标记数目最多。它们是什么，它们代表什么？\n",
    "3. 按频率递减的顺序列出标记。前20个最频繁的标记代表什么？\n",
    "4. 名词后面最常见的是哪些标记？这些标记代表什么？"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9247e0eb",
   "metadata": {},
   "source": [
    "16. ◑ 探索有关查找标注器的以下问题："
   ]
  },
  {
   "cell_type": "markdown",
   "id": "560174b8",
   "metadata": {},
   "source": [
    "1. 回退标注器被省略时，模型大小变化，标注器的准确性会发生什么？\n",
    "2. 思考[4.2](https://usyiyi.github.io/nlp-py-2e-zh/5.html#fig-tag-lookup)的曲线；为查找标注器推荐一个平衡内存和准确性的好的规模。你能想出在什么情况下应该尽量减少内存使用，什么情况下性能最大化而不必考虑内存使用？"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14588037",
   "metadata": {},
   "source": [
    "17. ◑ 查找标注器的准确性上限是什么，假设其表的大小没有限制？（提示：写一个程序算出被分配了最有可能的标记的词的词符的平均百分比。）\n",
    "\n",
    "18. ◑ 生成已标注数据的一些统计数据，回答下列问题："
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a64270b8",
   "metadata": {},
   "source": [
    "1. 总是被分配相同词性的词类的比例是多少？\n",
    "2. 多少词是有歧义的，从某种意义上说，它们至少和两个标记一起出现？\n",
    "3. 布朗语料库中这些有歧义的词的*词符*的百分比是多少？"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a52d8d9",
   "metadata": {},
   "source": [
    "19. ◑ `evaluate()`方法算出一个文本上运行的标注器的精度。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f296579",
   "metadata": {},
   "source": [
    "例如，如果提供的已标注文本是`[('the', 'DT'), ('dog', 'NN')]`，标注器产生的输出是`[('the', 'NN'), ('dog', 'NN')]`，那么得分为`0.5`。\n",
    "\n",
    "让我们尝试找出评价方法是如何工作的：\n",
    "\n",
    "1. 一个标注器`t`将一个词汇列表作为输入，产生一个已标注词列表作为输出。然而，`t.evaluate()`只以一个正确标注的文本作为唯一的参数。执行标注之前必须对输入做些什么？\n",
    "2. 一旦标注器创建了新标注的文本，`evaluate()` 方法可能如何比较它与原来标注的文本，计算准确性得分？\n",
    "3. 现在，检查源代码来看看这个方法是如何实现的。检查`nltk.tag.api.__file__`找到源代码的位置，使用编辑器打开这个文件（一定要使用文件`api.py`，而不是编译过的二进制文件`api.pyc`）。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eee3c9f9",
   "metadata": {},
   "source": [
    "20. ◑ 编写代码，搜索布朗语料库，根据标记查找特定的词和短语，回答下列问题："
   ]
  },
  {
   "cell_type": "markdown",
   "id": "483a09c2",
   "metadata": {},
   "source": [
    "1. 产生一个标注为`MD`的不同的词的按字母顺序排序的列表。\n",
    "2. 识别可能是复数名词或第三人称单数动词的词（如deals, flies）。\n",
    "3. 识别三个词的介词短语形式IN + DET + NN（如in the lab）。\n",
    "4. 男性与女性代词的比例是多少？"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb51a1ac",
   "metadata": {},
   "source": [
    "21. ◑ 在[3.1](https://usyiyi.github.io/nlp-py-2e-zh/3.html#tab-absolutely)中我们看到动词adore, love, like, prefer及前面的限定符absolutely和definitely的频率计数的表格。探讨这四个动词前出现的所有限定符。\n",
    "\n",
    "22. ◑ 我们定义可以用来做生词的回退标注器的`regexp_tagger`。这个标注器只检查基数词。通过特定的前缀或后缀字符串进行测试，它应该能够猜测其他标记。例如，我们可以标注所有-s结尾的词为复数名词。定义一个正则表达式标注器（使用`RegexpTagger()`），测试至少5 个单词拼写的其他模式。（使用内联文档解释规则。）\n",
    "\n",
    "23. ◑ 考虑上一练习中开发的正则表达式标注器。使用它的`accuracy()`方法评估标注器，尝试想办法提高其性能。讨论你的发现。客观的评估如何帮助开发过程？\n",
    "\n",
    "24. ◑ 数据稀疏问题有多严重？调查n-gram 标注器当n从1增加到6时的准确性。为准确性得分制表。估计这些标注器需要的训练数据，假设词汇量大小为105而标记集的大小为102。\n",
    "\n",
    "25. ◑ 获取另一种语言的一些已标注数据，在其上测试和评估各种标注器。如果这种语言是形态复杂的，或者有词类的任何字形线索（如），可以考虑为它开发一个正则表达式标注器（排在一元标注器之后，默认标注器之前）。对比同样的运行在英文数据上的标注器，你的标注器的准确性如何？讨论你在运用这些方法到这种语言时遇到的问题。\n",
    "\n",
    "26. ◑ [4.1](https://usyiyi.github.io/nlp-py-2e-zh/5.html#code-baseline-tagger)绘制曲线显示查找标注器的性能随模型的大小增加的变化。绘制当训练数据量变化时一元标注器的性能曲线。\n",
    "\n",
    "27. ◑ 检查[5](https://usyiyi.github.io/nlp-py-2e-zh/5.html#sec-n-gram-tagging)中定义的二元标注器`t2`的混淆矩阵，确定简化的一套或多套标记。定义字典做映射，在简化的数据上评估标注器。\n",
    "\n",
    "28. ◑ 使用简化的标记集测试标注器（或制作一个你自己的，通过丢弃每个标记名中除第一个字母外所有的字母）。这种标注器需要做的区分更少，但由它获得的信息也更少。讨论你的发现。\n",
    "\n",
    "29. ◑ 回顾一个二元标注器训练过程中遇到生词，标注句子的其余部分为`None`的例子。一个二元标注器可能只处理了句子的一部分就失败了，即使句子中没有包含生词（即使句子在训练过程中使用过）。在什么情况下会出现这种情况呢？你可以写一个程序，找到一些这方面的例子吗？\n",
    "\n",
    "30. ◑ 预处理布朗新闻数据，替换低频词为UNK，但留下标记不变。在这些数据上训练和评估一个二元标注器。这样有多少帮助？一元标注器和默认标注器的贡献是什么？\n",
    "\n",
    "31. ◑ 修改[4.1](https://usyiyi.github.io/nlp-py-2e-zh/5.html#code-baseline-tagger)中的程序，通过将`pylab.plot()`替换为`pylab.semilogx()`，在*x*轴上使用对数刻度。关于结果图形的形状，你注意到了什么？梯度告诉你什么呢？\n",
    "\n",
    "32. ◑ 使用`help(nltk.tag.brill.demo)`阅读Brill标注器演示函数的文档。通过设置不同的参数值试验这个标注器。是否有任何训练时间（语料库大小）和性能之间的权衡？\n",
    "\n",
    "33. ◑ 写代码构建一个集合的字典的字典。用它来存储一套可以跟在具有给定词性标记的给定词后面的词性标记，例如wordi → tagi → tagi+1。\n",
    "\n",
    "34. ★ 布朗语料库中有264个不同的词有3种可能的标签。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2c3ce0c",
   "metadata": {},
   "source": [
    "1. 打印一个表格，一列中是整数1..10，另一列是语料库中有1..10个不同标记的不同词的数目。\n",
    "2. 对有不同的标记数量最多的词，输出语料库中包含这个词的句子，每个可能的标记一个。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37f0d32e",
   "metadata": {},
   "source": [
    "35. ★ 写一个程序，按照词must后面的词的标记为它的上下文分类。这样可以区分must的“必须”和“应该”两种词意上的用法吗？\n",
    "\n",
    "36. ★ 创建一个正则表达式标注器和各种一元以及n-gram标注器，包括回退，在布朗语料库上训练它们。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30116f97",
   "metadata": {},
   "source": [
    "1. 创建这些标注器的3种不同组合。测试每个组合标注器的准确性。哪种组合效果最好？\n",
    "2. 尝试改变训练语料的规模。它是如何影响你的结果的？"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc9b4fbb",
   "metadata": {},
   "source": [
    "37. ★ 我们标注生词的方法一直要考虑这个词的字母（使用`RegexpTagger()`），或完全忽略这个词，将它标注为一个名词（使用`nltk.DefaultTagger()`）。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a67bf8a8",
   "metadata": {},
   "source": [
    "这些方法对于有新词却不是名词的文本不会很好。\n",
    "\n",
    "思考句子I like to blog on Kim's blog。\n",
    "\n",
    "如果blog是一个新词，那么查看前面的标记（`TO`和`NP$`）可能会有所帮助。\n",
    "\n",
    "即\n",
    "\n",
    "我们需要一个对前面的标记敏感的默认标注器。\n",
    "\n",
    "1. 创建一种新的一元标注器，查看前一个词的标记，而忽略当前词。（做到这一点的最好办法是修改`UnigramTagger()`的源代码，需要Python中的面向对象编程的知识。\n",
    "2. 将这个标注器加入到回退标注器序列（包括普通的三元和二元标注器），放在常用默认标注器的前面。\n",
    "3. 评价这个新的一元标注器的贡献。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "898b3333",
   "metadata": {},
   "source": [
    "38. ★ 思考[5](https://usyiyi.github.io/nlp-py-2e-zh/5.html#sec-n-gram-tagging)中的代码，它确定一个三元标注器的准确性上限。回顾Abney的关于精确标注的不可能性的讨论[(Church, Young, & Bloothooft, 1996)](https://usyiyi.github.io/nlp-py-2e-zh/bibliography.html#abney1996pst)。解释为什么正确标注这些例子需要获取词和标记以外的其他种类的信息。你如何估计这个问题的规模？\n",
    "\n",
    "39. ★ 使用`nltk.probability`中的一些估计技术，例如*Lidstone*或*Laplace* 估计，开发一种统计标注器，它在训练中没有遇到而测试中遇到的上下文中表现优于n-gram回退标注器。\n",
    "\n",
    "40. ★ 检查Brill标注器创建的诊断文件`rules.out`和`errors.out`。通过访问源代码（`http://www.nltk.org/code`）获得演示代码，创建你自己版本的Brill标注器。并根据你从检查`rules.out`了解到的，删除一些规则模板。增加一些新的规则模板，这些模板使用那些可能有助于纠正你在`errors.out`看到的错误的上下文。\n",
    "\n",
    "41. ★ 开发一个n-gram回退标注器，允许在标注器初始化时指定“anti-n-grams”，如`[\"the\", \"the\"]`。一个anti-n-grams被分配一个数字0，被用来防止这个n-gram回退（如避免估计P(the | the)而只做P(the)）。\n",
    "\n",
    "42. ★ 使用布朗语料库开发标注器时，调查三种不同的方式来定义训练和测试数据之间的分割：genre (`category`)、source (`fileid`)和句子。比较它们的相对性能，并讨论哪种方法最合理。（你可能要使用n-交叉验证，在[3](https://usyiyi.github.io/nlp-py-2e-zh/6.html#sec-evaluation)中讨论的，以提高评估的准确性。）\n",
    "\n",
    "43. ★ 开发你自己的`NgramTagger`，从NLTK中的类继承，封装本章中所述的已标注的训练和测试数据的词汇表缩减方法。确保一元和默认回退标注器有机会获得全部词汇。\n",
    "\n",
    "关于本文档\n",
    "\n",
    "UPDATED FOR NLTK 3.0. 本章来自于*Natural Language Processing with Python*，[Steven Bird](http://estive.net/), [Ewan Klein](http://homepages.inf.ed.ac.uk/ewan/) 和[Edward Loper](http://ed.loper.org/)，Copyright © 2014 作者所有。本章依据*Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License* [http://creativecommons.org/licenses/by-nc-nd/3.0/us/] 条款，与*自然语言工具包* [`http://nltk.org/`] 3.0 版一起发行。\n",
    "\n",
    "本文档构建于星期三 2015 年 7 月 1 日 12:30:05 AEST"
   ]
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