chapter04.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "chapter04.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QZRQ6oc6zmL3",
        "colab_type": "text"
      },
      "source": [
        "# 第4章: 形態素解析\n",
        "夏目漱石の小説『吾輩は猫である』の文章（neko.txt）をMeCabを使って形態素解析し，その結果をneko.txt.mecabというファイルに保存せよ．このファイルを用いて，以下の問に対応するプログラムを実装せよ．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PXzCuUAl1JI9",
        "colab_type": "code",
        "outputId": "db30d049-abca-4837-96d9-52c2a4b880c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        }
      },
      "source": [
        "!wget https://nlp100.github.io/data/neko.txt"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-06-13 12:30:20--  https://nlp100.github.io/data/neko.txt\n",
            "Resolving nlp100.github.io (nlp100.github.io)... 185.199.109.153, 185.199.110.153, 185.199.108.153, ...\n",
            "Connecting to nlp100.github.io (nlp100.github.io)|185.199.109.153|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 975789 (953K) [text/plain]\n",
            "Saving to: ‘neko.txt’\n",
            "\n",
            "\rneko.txt              0%[                    ]       0  --.-KB/s               \rneko.txt            100%[===================>] 952.92K  --.-KB/s    in 0.06s   \n",
            "\n",
            "2020-06-13 12:30:21 (14.4 MB/s) - ‘neko.txt’ saved [975789/975789]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jNgXnfi81Kr9",
        "colab_type": "code",
        "outputId": "cb8c751e-dfc7-4fc5-9946-3b8b7a7eb414",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!apt install mecab libmecab-dev mecab-ipadic-utf8"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-440\n",
            "Use 'apt autoremove' to remove it.\n",
            "The following additional packages will be installed:\n",
            "  libmecab2 mecab-ipadic mecab-jumandic mecab-jumandic-utf8 mecab-utils\n",
            "The following NEW packages will be installed:\n",
            "  libmecab-dev libmecab2 mecab mecab-ipadic mecab-ipadic-utf8 mecab-jumandic\n",
            "  mecab-jumandic-utf8 mecab-utils\n",
            "0 upgraded, 8 newly installed, 0 to remove and 43 not upgraded.\n",
            "Need to get 29.0 MB of archives.\n",
            "After this operation, 277 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libmecab2 amd64 0.996-5 [257 kB]\n",
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            "Get:4 http://archive.ubuntu.com/ubuntu bionic/universe amd64 mecab-jumandic-utf8 all 7.0-20130310-4 [16.2 MB]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu bionic/universe amd64 mecab-jumandic all 7.0-20130310-4 [2,212 B]\n",
            "Get:6 http://archive.ubuntu.com/ubuntu bionic/universe amd64 mecab-ipadic all 2.7.0-20070801+main-1 [12.1 MB]\n",
            "Get:7 http://archive.ubuntu.com/ubuntu bionic/universe amd64 mecab amd64 0.996-5 [132 kB]\n",
            "Get:8 http://archive.ubuntu.com/ubuntu bionic/universe amd64 mecab-ipadic-utf8 all 2.7.0-20070801+main-1 [3,522 B]\n",
            "Fetched 29.0 MB in 1s (51.9 MB/s)\n",
            "Selecting previously unselected package libmecab2:amd64.\n",
            "(Reading database ... 144328 files and directories currently installed.)\n",
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            "Preparing to unpack .../5-mecab-ipadic_2.7.0-20070801+main-1_all.deb ...\n",
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            "Unpacking mecab (0.996-5) ...\n",
            "Selecting previously unselected package mecab-ipadic-utf8.\n",
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            "Unpacking mecab-ipadic-utf8 (2.7.0-20070801+main-1) ...\n",
            "Setting up libmecab2:amd64 (0.996-5) ...\n",
            "Setting up mecab-utils (0.996-5) ...\n",
            "Setting up mecab-ipadic (2.7.0-20070801+main-1) ...\n",
            "Compiling IPA dictionary for Mecab.  This takes long time...\n",
            "reading /usr/share/mecab/dic/ipadic/unk.def ... 40\n",
            "emitting double-array: 100% |###########################################| \n",
            "/usr/share/mecab/dic/ipadic/model.def is not found. skipped.\n",
            "reading /usr/share/mecab/dic/ipadic/Auxil.csv ... 199\n",
            "reading /usr/share/mecab/dic/ipadic/Symbol.csv ... 208\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.place.csv ... 72999\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.name.csv ... 34202\n",
            "reading /usr/share/mecab/dic/ipadic/Verb.csv ... 130750\n",
            "reading /usr/share/mecab/dic/ipadic/Others.csv ... 2\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.demonst.csv ... 120\n",
            "reading /usr/share/mecab/dic/ipadic/Suffix.csv ... 1393\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.others.csv ... 151\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.number.csv ... 42\n",
            "reading /usr/share/mecab/dic/ipadic/Filler.csv ... 19\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.nai.csv ... 42\n",
            "reading /usr/share/mecab/dic/ipadic/Adj.csv ... 27210\n",
            "reading /usr/share/mecab/dic/ipadic/Prefix.csv ... 221\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.csv ... 60477\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.proper.csv ... 27327\n",
            "reading /usr/share/mecab/dic/ipadic/Conjunction.csv ... 171\n",
            "reading /usr/share/mecab/dic/ipadic/Postp.csv ... 146\n",
            "reading /usr/share/mecab/dic/ipadic/Adnominal.csv ... 135\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.verbal.csv ... 12146\n",
            "reading /usr/share/mecab/dic/ipadic/Interjection.csv ... 252\n",
            "reading /usr/share/mecab/dic/ipadic/Postp-col.csv ... 91\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.org.csv ... 16668\n",
            "reading /usr/share/mecab/dic/ipadic/Adverb.csv ... 3032\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.adjv.csv ... 3328\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.adverbal.csv ... 795\n",
            "emitting double-array: 100% |###########################################| \n",
            "reading /usr/share/mecab/dic/ipadic/matrix.def ... 1316x1316\n",
            "emitting matrix      : 100% |###########################################| \n",
            "\n",
            "done!\n",
            "update-alternatives: using /var/lib/mecab/dic/ipadic to provide /var/lib/mecab/dic/debian (mecab-dictionary) in auto mode\n",
            "Setting up libmecab-dev (0.996-5) ...\n",
            "Setting up mecab-jumandic-utf8 (7.0-20130310-4) ...\n",
            "Compiling Juman dictionary for Mecab.\n",
            "reading /usr/share/mecab/dic/juman/unk.def ... 37\n",
            "emitting double-array: 100% |###########################################| \n",
            "reading /usr/share/mecab/dic/juman/ContentW.csv ... 551145\n",
            "reading /usr/share/mecab/dic/juman/Noun.suusi.csv ... 49\n",
            "reading /usr/share/mecab/dic/juman/Rengo.csv ... 1118\n",
            "reading /usr/share/mecab/dic/juman/Suffix.csv ... 2128\n",
            "reading /usr/share/mecab/dic/juman/Noun.koyuu.csv ... 7964\n",
            "reading /usr/share/mecab/dic/juman/Prefix.csv ... 90\n",
            "reading /usr/share/mecab/dic/juman/Demonstrative.csv ... 97\n",
            "reading /usr/share/mecab/dic/juman/Wikipedia.csv ... 167709\n",
            "reading /usr/share/mecab/dic/juman/Noun.hukusi.csv ... 81\n",
            "reading /usr/share/mecab/dic/juman/Assert.csv ... 34\n",
            "reading /usr/share/mecab/dic/juman/Special.csv ... 158\n",
            "reading /usr/share/mecab/dic/juman/Auto.csv ... 18931\n",
            "reading /usr/share/mecab/dic/juman/Emoticon.csv ... 972\n",
            "reading /usr/share/mecab/dic/juman/AuxV.csv ... 593\n",
            "reading /usr/share/mecab/dic/juman/Postp.csv ... 108\n",
            "reading /usr/share/mecab/dic/juman/Noun.keishiki.csv ... 8\n",
            "emitting double-array: 100% |###########################################| \n",
            "reading /usr/share/mecab/dic/juman/matrix.def ... 1876x1876\n",
            "emitting matrix      : 100% |###########################################| \n",
            "\n",
            "done!\n",
            "Setting up mecab-ipadic-utf8 (2.7.0-20070801+main-1) ...\n",
            "Compiling IPA dictionary for Mecab.  This takes long time...\n",
            "reading /usr/share/mecab/dic/ipadic/unk.def ... 40\n",
            "emitting double-array: 100% |###########################################| \n",
            "/usr/share/mecab/dic/ipadic/model.def is not found. skipped.\n",
            "reading /usr/share/mecab/dic/ipadic/Auxil.csv ... 199\n",
            "reading /usr/share/mecab/dic/ipadic/Symbol.csv ... 208\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.place.csv ... 72999\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.name.csv ... 34202\n",
            "reading /usr/share/mecab/dic/ipadic/Verb.csv ... 130750\n",
            "reading /usr/share/mecab/dic/ipadic/Others.csv ... 2\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.demonst.csv ... 120\n",
            "reading /usr/share/mecab/dic/ipadic/Suffix.csv ... 1393\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.others.csv ... 151\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.number.csv ... 42\n",
            "reading /usr/share/mecab/dic/ipadic/Filler.csv ... 19\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.nai.csv ... 42\n",
            "reading /usr/share/mecab/dic/ipadic/Adj.csv ... 27210\n",
            "reading /usr/share/mecab/dic/ipadic/Prefix.csv ... 221\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.csv ... 60477\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.proper.csv ... 27327\n",
            "reading /usr/share/mecab/dic/ipadic/Conjunction.csv ... 171\n",
            "reading /usr/share/mecab/dic/ipadic/Postp.csv ... 146\n",
            "reading /usr/share/mecab/dic/ipadic/Adnominal.csv ... 135\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.verbal.csv ... 12146\n",
            "reading /usr/share/mecab/dic/ipadic/Interjection.csv ... 252\n",
            "reading /usr/share/mecab/dic/ipadic/Postp-col.csv ... 91\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.org.csv ... 16668\n",
            "reading /usr/share/mecab/dic/ipadic/Adverb.csv ... 3032\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.adjv.csv ... 3328\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.adverbal.csv ... 795\n",
            "emitting double-array: 100% |###########################################| \n",
            "reading /usr/share/mecab/dic/ipadic/matrix.def ... 1316x1316\n",
            "emitting matrix      : 100% |###########################################| \n",
            "\n",
            "done!\n",
            "update-alternatives: using /var/lib/mecab/dic/ipadic-utf8 to provide /var/lib/mecab/dic/debian (mecab-dictionary) in auto mode\n",
            "Setting up mecab (0.996-5) ...\n",
            "Compiling IPA dictionary for Mecab.  This takes long time...\n",
            "reading /usr/share/mecab/dic/ipadic/unk.def ... 40\n",
            "emitting double-array: 100% |###########################################| \n",
            "/usr/share/mecab/dic/ipadic/model.def is not found. skipped.\n",
            "reading /usr/share/mecab/dic/ipadic/Auxil.csv ... 199\n",
            "reading /usr/share/mecab/dic/ipadic/Symbol.csv ... 208\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.place.csv ... 72999\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.name.csv ... 34202\n",
            "reading /usr/share/mecab/dic/ipadic/Verb.csv ... 130750\n",
            "reading /usr/share/mecab/dic/ipadic/Others.csv ... 2\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.demonst.csv ... 120\n",
            "reading /usr/share/mecab/dic/ipadic/Suffix.csv ... 1393\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.others.csv ... 151\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.number.csv ... 42\n",
            "reading /usr/share/mecab/dic/ipadic/Filler.csv ... 19\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.nai.csv ... 42\n",
            "reading /usr/share/mecab/dic/ipadic/Adj.csv ... 27210\n",
            "reading /usr/share/mecab/dic/ipadic/Prefix.csv ... 221\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.csv ... 60477\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.proper.csv ... 27327\n",
            "reading /usr/share/mecab/dic/ipadic/Conjunction.csv ... 171\n",
            "reading /usr/share/mecab/dic/ipadic/Postp.csv ... 146\n",
            "reading /usr/share/mecab/dic/ipadic/Adnominal.csv ... 135\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.verbal.csv ... 12146\n",
            "reading /usr/share/mecab/dic/ipadic/Interjection.csv ... 252\n",
            "reading /usr/share/mecab/dic/ipadic/Postp-col.csv ... 91\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.org.csv ... 16668\n",
            "reading /usr/share/mecab/dic/ipadic/Adverb.csv ... 3032\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.adjv.csv ... 3328\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.adverbal.csv ... 795\n",
            "emitting double-array: 100% |###########################################| \n",
            "reading /usr/share/mecab/dic/ipadic/matrix.def ... 1316x1316\n",
            "emitting matrix      : 100% |###########################################| \n",
            "\n",
            "done!\n",
            "Setting up mecab-jumandic (7.0-20130310-4) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/python3.6/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e1K64W9y2VY-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mecab -o ./neko.txt.mecab ./neko.txt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DIVJF5m6WnGm",
        "colab_type": "code",
        "outputId": "ddcb4e82-5dbe-4f6d-ae2c-e1b26e4aa656",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "!wc -l ./neko.txt.mecab"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "226266 ./neko.txt.mecab\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0KfX_cgYWreY",
        "colab_type": "code",
        "outputId": "e92d2ccd-83d2-4bb3-857e-dc4e28c6fc63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "!head -15 ./neko.txt.mecab"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "一\t名詞,数,*,*,*,*,一,イチ,イチ\n",
            "\r\t記号,一般,*,*,*,*,*\n",
            "EOS\n",
            "\r\t記号,一般,*,*,*,*,*\n",
            "EOS\n",
            "　\t記号,空白,*,*,*,*,　,　,　\n",
            "吾輩\t名詞,代名詞,一般,*,*,*,吾輩,ワガハイ,ワガハイ\n",
            "は\t助詞,係助詞,*,*,*,*,は,ハ,ワ\n",
            "猫\t名詞,一般,*,*,*,*,猫,ネコ,ネコ\n",
            "で\t助動詞,*,*,*,特殊・ダ,連用形,だ,デ,デ\n",
            "ある\t助動詞,*,*,*,五段・ラ行アル,基本形,ある,アル,アル\n",
            "。\t記号,句点,*,*,*,*,。,。,。\n",
            "\r\t記号,一般,*,*,*,*,*\n",
            "EOS\n",
            "名前\t名詞,一般,*,*,*,*,名前,ナマエ,ナマエ\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bZCzED2vzuPR",
        "colab_type": "text"
      },
      "source": [
        "## 30. 形態素解析結果の読み込み\n",
        "***\n",
        "形態素解析結果（neko.txt.mecab）を読み込むプログラムを実装せよ．ただし，各形態素は表層形（surface），基本形（base），品詞（pos），品詞細分類1（pos1）をキーとするマッピング型に格納し，1文を形態素（マッピング型）のリストとして表現せよ．第4章の残りの問題では，ここで作ったプログラムを活用せよ．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zVEjIFvmQT7L",
        "colab_type": "code",
        "outputId": "3eba0a51-b0dc-4e4a-bc6f-f6f4a9df69f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "source": [
        "filename = './neko.txt.mecab'\n",
        "\n",
        "sentences = []\n",
        "morphs = []\n",
        "with open(filename, mode='r') as f:\n",
        "  for line in f:  # 1行ずつ読込\n",
        "    if line != 'EOS\\n':  # 文末以外：形態素解析情報を辞書型に格納して形態素リストに追加\n",
        "      fields = line.split('\\t')\n",
        "      if len(fields) != 2 or fields[0] == '':\n",
        "        continue\n",
        "      else:\n",
        "        attr =  fields[1].split(',')\n",
        "        morph = {'surface': fields[0], 'base': attr[6], 'pos': attr[0], 'pos1': attr[1]}\n",
        "        morphs.append(morph)\n",
        "    else:  # 文末：形態素リストを文リストに追加\n",
        "      sentences.append(morphs)\n",
        "      morphs = []\n",
        "\n",
        "# 確認\n",
        "for morph in sentences[2]:\n",
        "  print(morph)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'surface': '\\u3000', 'base': '\\u3000', 'pos': '記号', 'pos1': '空白'}\n",
            "{'surface': '吾輩', 'base': '吾輩', 'pos': '名詞', 'pos1': '代名詞'}\n",
            "{'surface': 'は', 'base': 'は', 'pos': '助詞', 'pos1': '係助詞'}\n",
            "{'surface': '猫', 'base': '猫', 'pos': '名詞', 'pos1': '一般'}\n",
            "{'surface': 'で', 'base': 'だ', 'pos': '助動詞', 'pos1': '*'}\n",
            "{'surface': 'ある', 'base': 'ある', 'pos': '助動詞', 'pos1': '*'}\n",
            "{'surface': '。', 'base': '。', 'pos': '記号', 'pos1': '句点'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bofBhDZLzy5_",
        "colab_type": "text"
      },
      "source": [
        "## 31. 動詞\n",
        "***\n",
        "動詞の表層形をすべて抽出せよ．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J8ew9yTRz2If",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "020b28ba-0e80-4904-bb88-2bf20d93ee47"
      },
      "source": [
        "ans = set()\n",
        "for sentence in sentences:\n",
        "  for morph in sentence:\n",
        "    if morph['pos'] == '動詞':\n",
        "      ans.add(morph['surface'])  # set型なので重複しない要素のみ保持していく\n",
        "\n",
        "# 確認\n",
        "print(f'動詞の表層形の種類: {len(ans)}\\n')\n",
        "print('---サンプル---')\n",
        "for v in list(ans)[:10]:\n",
        "  print(v)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "動詞の表層形の種類: 3893\n",
            "\n",
            "---サンプル---\n",
            "ねだん\n",
            "待たさ\n",
            "貪\n",
            "祟っ\n",
            "突きつけ\n",
            "浮ば\n",
            "叱る\n",
            "してやろ\n",
            "保つ\n",
            "能わ\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p5DSX-wXz2rz",
        "colab_type": "text"
      },
      "source": [
        "## 32. 動詞の原形\n",
        "***\n",
        "動詞の原形をすべて抽出せよ．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YDdEm0Evz6AF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "6ed63b13-7875-49d8-b639-fbe108fb3da3"
      },
      "source": [
        "ans = set()\n",
        "for sentence in sentences:\n",
        "  for morph in sentence:\n",
        "    if morph['pos'] == '動詞':\n",
        "      ans.add(morph['base'])\n",
        "\n",
        "# 確認\n",
        "print(f'動詞の原形の種類: {len(ans)}\\n')\n",
        "print('---サンプル---')\n",
        "for v in list(ans)[:10]:\n",
        "  print(v)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "動詞の原形の種類: 2300\n",
            "\n",
            "---サンプル---\n",
            "煮える\n",
            "叱る\n",
            "保つ\n",
            "じみる\n",
            "素っ破抜く\n",
            "吸取る\n",
            "思い込む\n",
            "気に入る\n",
            "がする\n",
            "恐れる\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ot2yShm3z6l2",
        "colab_type": "text"
      },
      "source": [
        "## 33. 「AのB」\n",
        "***\n",
        "2つの名詞が「の」で連結されている名詞句を抽出せよ．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SLRNpvh_Y3VV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "d04ab684-8403-4cfe-d860-d75986592989"
      },
      "source": [
        "ans = set()\n",
        "for sentence in sentences:\n",
        "  for i in range(1, len(sentence) - 1):\n",
        "    if sentence[i - 1]['pos'] == '名詞' and sentence[i]['surface'] == 'の' and sentence[i + 1]['pos'] == '名詞':\n",
        "      ans.add(sentence[i - 1]['surface'] + sentence[i]['surface'] + sentence[i + 1]['surface'])\n",
        "\n",
        "# 確認\n",
        "print(f'「名詞+の+名詞」の種類: {len(ans)}\\n')\n",
        "print('---サンプル---')\n",
        "for n in list(ans)[:10]:\n",
        "  print(n)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "「名詞+の+名詞」の種類: 4924\n",
            "\n",
            "---サンプル---\n",
            "鼻の上\n",
            "研究の材料\n",
            "倫理の講義\n",
            "何の真似\n",
            "泥棒のよう\n",
            "性行の一\n",
            "家の動静\n",
            "八の女学生\n",
            "方面の研究\n",
            "歳の時\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hsh_zjTyz_jq",
        "colab_type": "text"
      },
      "source": [
        "## 34. 名詞の連接\n",
        "***\n",
        "名詞の連接（連続して出現する名詞）を最長一致で抽出せよ．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KKgjjSBmZZ1k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "b4ec80e8-9beb-491c-f954-57d4195e5686"
      },
      "source": [
        "ans = set()\n",
        "for sentence in sentences:\n",
        "  nouns = ''\n",
        "  num = 0\n",
        "  for morph in sentence:\n",
        "    if morph['pos'] == '名詞':  # 最初の形態素から順に、名詞であればnounsに連結し、連結数(num)をカウント\n",
        "      nouns = ''.join([nouns, morph['surface']])\n",
        "      num += 1\n",
        "    elif num >= 2:  # 名詞以外、かつここまでの連結数が2以上の場合は出力し、nounsとnumを初期化\n",
        "      ans.add(nouns)\n",
        "      nouns = ''\n",
        "      num = 0\n",
        "    else:  # それ以外の場合、nounsとnumを初期化\n",
        "      nouns = ''\n",
        "      num = 0\n",
        "\n",
        "# 確認\n",
        "print(f'連接名詞の種類: {len(ans)}\\n')\n",
        "print('---サンプル---')\n",
        "for n in list(ans)[:10]:\n",
        "  print(n)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "連接名詞の種類: 4454\n",
            "\n",
            "---サンプル---\n",
            "連判状\n",
            "あら人\n",
            "寒月君首縊り\n",
            "細君相形\n",
            "評隲\n",
            "倉皇針箱\n",
            "逍遥派\n",
            "蒲鉾町\n",
            "——紺屋橋\n",
            "時吾輩\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9tKKLA9c0E_J",
        "colab_type": "text"
      },
      "source": [
        "## 35. 単語の出現頻度\n",
        "***\n",
        "文章中に出現する単語とその出現頻度を求め，出現頻度の高い順に並べよ．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vm4-5kFo0Ibw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "c3d49a77-20c3-476d-c93b-b7356c7c6444"
      },
      "source": [
        "from collections import defaultdict \n",
        "\n",
        "ans = defaultdict(int)\n",
        "for sentence in sentences:\n",
        "  for morph in sentence:\n",
        "    if morph['pos'] != '記号':\n",
        "      ans[morph['base']] += 1  # 単語数の更新(初登場の単語であれば1をセット)\n",
        "ans = sorted(ans.items(), key=lambda x: x[1], reverse=True)\n",
        "\n",
        "# 確認\n",
        "for w in ans[:10]:\n",
        "  print(w)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "('の', 9194)\n",
            "('て', 6848)\n",
            "('は', 6420)\n",
            "('に', 6243)\n",
            "('を', 6071)\n",
            "('だ', 5972)\n",
            "('と', 5508)\n",
            "('が', 5337)\n",
            "('た', 4267)\n",
            "('する', 3657)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ooKypowr0I-t",
        "colab_type": "text"
      },
      "source": [
        "## 36. 頻度上位10語\n",
        "***\n",
        "出現頻度が高い10語とその出現頻度をグラフ（例えば棒グラフなど）で表示せよ．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4fLA5hZVq5Dt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "771596d8-774d-4e8e-b2c4-aa0468f9012b"
      },
      "source": [
        "!pip install japanize_matplotlib"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting japanize_matplotlib\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/2c/aa/3b24d54bd02e25d63c8f23bb316694e1aad7ffdc07ba296e7c9be2f6837d/japanize-matplotlib-1.1.2.tar.gz (4.1MB)\n",
            "\u001b[K     |████████████████████████████████| 4.1MB 8.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from japanize_matplotlib) (3.2.1)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.6/dist-packages (from matplotlib->japanize_matplotlib) (1.18.5)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->japanize_matplotlib) (1.2.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->japanize_matplotlib) (2.4.7)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->japanize_matplotlib) (2.8.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->japanize_matplotlib) (0.10.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.1->matplotlib->japanize_matplotlib) (1.12.0)\n",
            "Building wheels for collected packages: japanize-matplotlib\n",
            "  Building wheel for japanize-matplotlib (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for japanize-matplotlib: filename=japanize_matplotlib-1.1.2-cp36-none-any.whl size=4120191 sha256=28184e63e6f47fc26cb7a857a52e63e6db1de8d79652e063041f8a7975f67e2c\n",
            "  Stored in directory: /root/.cache/pip/wheels/9c/f9/fc/bc052ce743a03f94ccc7fda73d1d389ce98216c6ffaaf65afc\n",
            "Successfully built japanize-matplotlib\n",
            "Installing collected packages: japanize-matplotlib\n",
            "Successfully installed japanize-matplotlib-1.1.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1q-85P8H0Mju",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "7168a016-cdcd-43e1-8007-ee5a4be701c6"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import japanize_matplotlib\n",
        "\n",
        "ans = defaultdict(int)\n",
        "for sentence in sentences:\n",
        "  for morph in sentence:\n",
        "    if morph['pos'] != '記号':\n",
        "      ans[morph['base']] += 1  # 単語数の更新(初登場の単語であれば1をセット)\n",
        "ans = sorted(ans.items(), key=lambda x: x[1], reverse=True)\n",
        "\n",
        "keys = [a[0] for a in ans[0:10]]\n",
        "values = [a[1] for a in ans[0:10]]\n",
        "plt.figure(figsize=(8, 4))\n",
        "plt.bar(keys, values)\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/japanize_matplotlib/japanize_matplotlib.py:15: MatplotlibDeprecationWarning: \n",
            "The createFontList function was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use FontManager.addfont instead.\n",
            "  font_list = font_manager.createFontList(font_files)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ewpsjX3N0NGO",
        "colab_type": "text"
      },
      "source": [
        "## 37. 「猫」と共起頻度の高い上位10語\n",
        "***\n",
        "「猫」とよく共起する（共起頻度が高い）10語とその出現頻度をグラフ（例えば棒グラフなど）で表示せよ．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AW4zvL-Z0Qql",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "outputId": "fefdbf01-6075-4b17-aef6-ee401be851c0"
      },
      "source": [
        "ans = defaultdict(int)\n",
        "for sentence in sentences:\n",
        "  if '猫' in [morph['surface'] for morph in sentence]:  # 文章の形態素に「猫」が含まれる場合のみ辞書に追加\n",
        "    for morph in sentence:\n",
        "      if morph['pos'] != '記号':\n",
        "        ans[morph['base']] += 1  # 単語数の更新(初登場の単語であれば1をセット)\n",
        "del ans['猫']\n",
        "ans = sorted(ans.items(), key=lambda x: x[1], reverse=True)\n",
        "\n",
        "keys = [a[0] for a in ans[0:10]]\n",
        "values = [a[1] for a in ans[0:10]]\n",
        "plt.figure(figsize=(8, 4))\n",
        "plt.bar(keys, values)\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ekAuBXB0RNo",
        "colab_type": "text"
      },
      "source": [
        "## 38. ヒストグラム\n",
        "***\n",
        "単語の出現頻度のヒストグラム（横軸に出現頻度，縦軸に出現頻度をとる単語の種類数を棒グラフで表したもの）を描け．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SfwByHgn0VfM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "outputId": "c1aef56e-3f55-4a15-d17a-7f2f674acce8"
      },
      "source": [
        "ans = defaultdict(int)\n",
        "for sentence in sentences:\n",
        "  for morph in sentence:\n",
        "    if morph['pos'] != '記号':\n",
        "      ans[morph['base']] += 1  # 単語数の更新(初登場の単語であれば1をセット)\n",
        "ans = ans.values()\n",
        "\n",
        "plt.figure(figsize=(8, 4))\n",
        "plt.hist(ans, bins=100)\n",
        "plt.xlabel('出現頻度')\n",
        "plt.ylabel('単語の種類数')\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gNTQBSNO0WD9",
        "colab_type": "text"
      },
      "source": [
        "## 39. Zipfの法則\n",
        "***\n",
        "単語の出現頻度順位を横軸，その出現頻度を縦軸として，両対数グラフをプロットせよ．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tb0pgRj20aRU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "ca2c4d19-3d91-433b-8a15-1783f12f0b51"
      },
      "source": [
        "import math\n",
        "\n",
        "ans = defaultdict(int)\n",
        "for sentence in sentences:\n",
        "  for morph in sentence:\n",
        "    if morph['pos'] != '記号':\n",
        "      ans[morph['base']] += 1\n",
        "ans = sorted(ans.items(), key=lambda x: x[1], reverse=True)\n",
        "\n",
        "ranks = [r + 1 for r in range(len(ans))]\n",
        "values = [a[1] for a in ans]\n",
        "plt.figure(figsize=(8, 4))\n",
        "plt.scatter(ranks, values)\n",
        "plt.xscale('log')\n",
        "plt.yscale('log')\n",
        "plt.xlabel('出現頻度順位')\n",
        "plt.ylabel('出現頻度')\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}