notebook/chapter04.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第4章: 形態素解析\n",
    "\n",
    "夏目漱石の小説『吾輩は猫である』の文章（[neko.txt](http://www.cl.ecei.tohoku.ac.jp/nlp100/data/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．\n",
    "\n",
    "なお，問題37, 38, 39は[matplotlib](http://matplotlib.org/)もしくは[Gnuplot](http://www.gnuplot.info/)を用いるとよい．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100  943k  100  943k    0     0  2849k      0 --:--:-- --:--:-- --:--:-- 2849k\n"
     ]
    }
   ],
   "source": [
    "from os import environ\n",
    "\n",
    "environ[\"NEKO_TXT\"] = \"./data/chapter04/neko.txt\"\n",
    "environ[\"NEKO_TXT_MECAB\"] = neko_txt_mecab = \"./data/chapter04/neko.txt.mecab\"\n",
    "\n",
    "!test -f $NEKO_TXT_MECAB || \\\n",
    "{ mkdir -p $(dirname $NEKO_TXT_MECAB) && \\\n",
    "  curl -o $NEKO_TXT http://www.cl.ecei.tohoku.ac.jp/nlp100/data/neko.txt && \\\n",
    "  mecab -d /usr/lib/x86_64-linux-gnu/mecab/dic/mecab-ipadic-neologd -o $NEKO_TXT_MECAB $NEKO_TXT; }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[{'base': '一', 'pos': '名詞', 'pos1': '数', 'surface': '一'}],\n",
      " [{'base': '\\u3000', 'pos': '記号', 'pos1': '空白', 'surface': '\\u3000'},\n",
      "  {'base': '吾輩は猫である', 'pos': '名詞', 'pos1': '固有名詞', 'surface': '吾輩は猫である'},\n",
      "  {'base': '。', 'pos': '記号', 'pos1': '句点', 'surface': '。'}],\n",
      " [{'base': '名前', 'pos': '名詞', 'pos1': '一般', 'surface': '名前'},\n",
      "  {'base': 'は', 'pos': '助詞', 'pos1': '係助詞', 'surface': 'は'},\n",
      "  {'base': 'まだ', 'pos': '副詞', 'pos1': '助詞類接続', 'surface': 'まだ'},\n",
      "  {'base': '無い', 'pos': '形容詞', 'pos1': '自立', 'surface': '無い'},\n",
      "  {'base': '。', 'pos': '記号', 'pos1': '句点', 'surface': '。'}]]\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "\n",
    "# MeCab format: 表層形 \\t 品詞, 品詞細分類1, 品詞細分類2, 品詞細分類3, 活用型, 活用形, 原形, 読み, 発音\n",
    "mecab_list = []\n",
    "with open(neko_txt_mecab, 'r', encoding='utf-8') as f:\n",
    "    sentence = []\n",
    "    for l in f:\n",
    "        if l.strip() == 'EOS':\n",
    "            if len(sentence):\n",
    "                mecab_list.append(sentence.copy())\n",
    "                sentence = []\n",
    "            continue\n",
    "        \n",
    "        surface, others = l.split('\\t', maxsplit=1)\n",
    "        others = others.split(',', maxsplit=8)\n",
    "        sentence.append({\n",
    "            'surface': surface,\n",
    "            'base': others[6],\n",
    "            'pos': others[0],\n",
    "            'pos1': others[1]\n",
    "        })\n",
    "\n",
    "pprint(mecab_list[:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 31. 動詞\n",
    "\n",
    "動詞の表層形をすべて抽出せよ．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "verb_surfaces: 3811\n"
     ]
    }
   ],
   "source": [
    "verb_surfaces = set()\n",
    "for s in mecab_list:\n",
    "    [verb_surfaces.add(m['surface']) for m in s if m['pos'] == '動詞']\n",
    "\n",
    "print(\"verb_surfaces:\", len(verb_surfaces))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "verb_bases: 2259\n"
     ]
    }
   ],
   "source": [
    "verb_bases = set()\n",
    "for s in mecab_list:\n",
    "    [verb_bases.add(m['base']) for m in s if m['pos'] == '動詞']\n",
    "\n",
    "print(\"verb_bases:\", len(verb_bases))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 33. サ変名詞\n",
    "\n",
    "サ変接続の名詞をすべて抽出せよ．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "noun_bases: 1293\n"
     ]
    }
   ],
   "source": [
    "noun_bases = set()\n",
    "for s in mecab_list:\n",
    "    [noun_bases.add(m['base']) for m in s if m['pos1'] == 'サ変接続']\n",
    "\n",
    "print(\"noun_bases:\", len(noun_bases))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 34. 「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",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "noun_phrases: 3132\n"
     ]
    }
   ],
   "source": [
    "noun_phrases = set()\n",
    "for s in mecab_list:\n",
    "    if len(s) < 3:\n",
    "        continue\n",
    "    \n",
    "    for i in range(len(s)-2):\n",
    "        m0, m1, m2 = s[i:i+3]\n",
    "        if m0['pos'] == m2['pos'] == \"名詞\" and m1['surface'] == \"の\":\n",
    "            noun_phrases.add(\"{}の{}\".format(m0['surface'], m2['surface']))\n",
    "            break\n",
    "\n",
    "print(\"noun_phrases:\", len(noun_phrases))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 35. 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",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "continuous_nouns_set: 1994\n"
     ]
    }
   ],
   "source": [
    "continuous_nouns_set = set()\n",
    "for s in mecab_list:\n",
    "    sentence_length = len(s)\n",
    "    for i in range(sentence_length-1):\n",
    "        if not s[i]['pos'] == \"名詞\":\n",
    "            continue\n",
    "        \n",
    "        cnouns = s[i]['surface']\n",
    "        offset = 1\n",
    "        while i+offset < sentence_length:\n",
    "            next_morpheme = s[i+offset]\n",
    "            if not next_morpheme['pos'] == \"名詞\":\n",
    "                break\n",
    "            cnouns = next_morpheme['surface']\n",
    "            offset += 1\n",
    "        \n",
    "        if offset > 1:\n",
    "            continuous_nouns_set.add(cnouns)\n",
    "\n",
    "print(\"continuous_nouns_set:\", len(continuous_nouns_set))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 36. 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",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('の', 9106),\n",
      " ('。', 7484),\n",
      " ('、', 6772),\n",
      " ('て', 6697),\n",
      " ('は', 6384),\n",
      " ('に', 6147),\n",
      " ('を', 6068),\n",
      " ('と', 5474),\n",
      " ('が', 5259),\n",
      " ('た', 3916)]\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "\n",
    "word_freq = {}\n",
    "for s in mecab_list:\n",
    "    for m in s:\n",
    "        surface = m['surface']\n",
    "        if surface in word_freq.keys():\n",
    "            word_freq[surface] += 1\n",
    "        else:\n",
    "            word_freq[surface] = 1\n",
    "\n",
    "word_freq = sorted(word_freq.items(), key=lambda kv: kv[1], reverse=True)\n",
    "pprint(word_freq[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 37. 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",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "word_freq_top10 = word_freq[:10]\n",
    "words = [t[0] for t in word_freq_top10]\n",
    "freqs = [int(t[1]) for t in word_freq_top10]\n",
    "\n",
    "plt.bar(words, freqs)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "freqs = [int(t[1]) for t in word_freq]\n",
    "\n",
    "plt.hist(freqs, range=(1,20), bins=20)\n",
    "plt.title(\"単語の出現頻度のヒストグラム\")\n",
    "plt.xlabel(\"出現頻度\")\n",
    "plt.ylabel(\"単語の種類数\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.xlabel(\"単語の出現頻度順位\")\n",
    "plt.ylabel(\"単語の出現頻度\")\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "\n",
    "plt.plot(range(1, len(freqs)+1), freqs)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
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