lecture13-형태소분석_POS_Taggers-구문분석_구_연어_시각화.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#형태소-분석---POS-Taggers\" data-toc-modified-id=\"형태소-분석---POS-Taggers-1\">형태소 분석 - POS Taggers</a></span><ul class=\"toc-item\"><li><span><a href=\"#품사-분석기-:-nltk.pos_tag()\" data-toc-modified-id=\"품사-분석기-:-nltk.pos_tag()-1.1\">품사 분석기 : nltk.pos_tag()</a></span></li><li><span><a href=\"#Tag-Sets\" data-toc-modified-id=\"Tag-Sets-1.2\">Tag Sets</a></span></li><li><span><a href=\"#untag\" data-toc-modified-id=\"untag-1.3\">untag</a></span></li><li><span><a href=\"#FreqDist()\" data-toc-modified-id=\"FreqDist()-1.4\">FreqDist()</a></span></li><li><span><a href=\"#gutenberg(대량의-말뭉치)로-POS-tagging\" data-toc-modified-id=\"gutenberg(대량의-말뭉치)로-POS-tagging-1.5\">gutenberg(대량의 말뭉치)로 POS tagging</a></span></li><li><span><a href=\"#한글-POS-Tagging\" data-toc-modified-id=\"한글-POS-Tagging-1.6\">한글 POS Tagging</a></span></li><li><span><a href=\"#한글-형태소-분석기-성능-비교\" data-toc-modified-id=\"한글-형태소-분석기-성능-비교-1.7\">한글 형태소 분석기 성능 비교</a></span></li><li><span><a href=\"#국립국어원-세종21-:-말뭉치-검색-기능\" data-toc-modified-id=\"국립국어원-세종21-:-말뭉치-검색-기능-1.8\">국립국어원 세종21 : 말뭉치 검색 기능</a></span></li><li><span><a href=\"#한글-실제-데이터(법전)-pos-tagging\" data-toc-modified-id=\"한글-실제-데이터(법전)-pos-tagging-1.9\">한글 실제 데이터(법전) pos tagging</a></span></li><li><span><a href=\"#naver-뉴스-기사-POS-tagging\" data-toc-modified-id=\"naver-뉴스-기사-POS-tagging-1.10\">naver 뉴스 기사 POS tagging</a></span></li><li><span><a href=\"#한글-POS-taggers-비교\" data-toc-modified-id=\"한글-POS-taggers-비교-1.11\">한글 POS taggers 비교</a></span></li></ul></li><li><span><a href=\"#Collocations\" data-toc-modified-id=\"Collocations-2\">Collocations</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 형태소 분석 - POS Taggers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- \"She sells seashells on the seashore.\"\n",
    "\n",
    "![POS_Tagging](./images/POS_Tagging.png)\n",
    "\n",
    "- 많은 형태소 분석기, 품사 Taggers 중에 적합한 것을 선택해야 함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import word_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"She sells seashells on the seashore.\"\n",
    "tokens = word_tokenize(sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['She', 'sells', 'seashells', 'on', 'the', 'seashore', '.']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 품사 분석기 : nltk.pos_tag()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "# nltk.download('averaged_perceptron_tagger')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk import pos_tag   # 형태소 분석기(품사 분석기) : tag set을 내장하고 있음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('She', 'PRP'),\n",
       " ('sells', 'VBZ'),\n",
       " ('seashells', 'NNS'),\n",
       " ('on', 'IN'),\n",
       " ('the', 'DT'),\n",
       " ('seashore', 'NN'),\n",
       " ('.', '.')]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_tag(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'She sells seashells on the seashore'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pattern = re.compile(r\"[{0}]\".format(re.escape(punctuation)))\n",
    "pattern.sub(\"\", sentence)   # 구두점 제거됨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "escape: .\n",
      "['she', 'sells', 'seashells', 'on', 'the', 'seashore']\n"
     ]
    }
   ],
   "source": [
    "result = []\n",
    "\n",
    "for token in tokens:\n",
    "    if pattern.search(token):\n",
    "        print(\"escape:\", token)\n",
    "    else:\n",
    "        result.append(token.lower())\n",
    "\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('she', 'PRP'),\n",
       " ('sells', 'VBZ'),\n",
       " ('seashells', 'NNS'),\n",
       " ('on', 'IN'),\n",
       " ('the', 'DT'),\n",
       " ('seashore', 'NN')]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_tag(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tag Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package tagsets to /Users/alex/nltk_data...\n",
      "[nltk_data]   Package tagsets is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('tagsets')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.help import brown_tagset, upenn_tagset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$: dollar\n",
      "    $ -$ --$ A$ C$ HK$ M$ NZ$ S$ U.S.$ US$\n",
      "'': closing quotation mark\n",
      "    ' ''\n",
      "(: opening parenthesis\n",
      "    ( [ {\n",
      "): closing parenthesis\n",
      "    ) ] }\n",
      ",: comma\n",
      "    ,\n",
      "--: dash\n",
      "    --\n",
      ".: sentence terminator\n",
      "    . ! ?\n",
      ":: colon or ellipsis\n",
      "    : ; ...\n",
      "CC: conjunction, coordinating\n",
      "    & 'n and both but either et for less minus neither nor or plus so\n",
      "    therefore times v. versus vs. whether yet\n",
      "CD: numeral, cardinal\n",
      "    mid-1890 nine-thirty forty-two one-tenth ten million 0.5 one forty-\n",
      "    seven 1987 twenty '79 zero two 78-degrees eighty-four IX '60s .025\n",
      "    fifteen 271,124 dozen quintillion DM2,000 ...\n",
      "DT: determiner\n",
      "    all an another any both del each either every half la many much nary\n",
      "    neither no some such that the them these this those\n",
      "EX: existential there\n",
      "    there\n",
      "FW: foreign word\n",
      "    gemeinschaft hund ich jeux habeas Haementeria Herr K'ang-si vous\n",
      "    lutihaw alai je jour objets salutaris fille quibusdam pas trop Monte\n",
      "    terram fiche oui corporis ...\n",
      "IN: preposition or conjunction, subordinating\n",
      "    astride among uppon whether out inside pro despite on by throughout\n",
      "    below within for towards near behind atop around if like until below\n",
      "    next into if beside ...\n",
      "JJ: adjective or numeral, ordinal\n",
      "    third ill-mannered pre-war regrettable oiled calamitous first separable\n",
      "    ectoplasmic battery-powered participatory fourth still-to-be-named\n",
      "    multilingual multi-disciplinary ...\n",
      "JJR: adjective, comparative\n",
      "    bleaker braver breezier briefer brighter brisker broader bumper busier\n",
      "    calmer cheaper choosier cleaner clearer closer colder commoner costlier\n",
      "    cozier creamier crunchier cuter ...\n",
      "JJS: adjective, superlative\n",
      "    calmest cheapest choicest classiest cleanest clearest closest commonest\n",
      "    corniest costliest crassest creepiest crudest cutest darkest deadliest\n",
      "    dearest deepest densest dinkiest ...\n",
      "LS: list item marker\n",
      "    A A. B B. C C. D E F First G H I J K One SP-44001 SP-44002 SP-44005\n",
      "    SP-44007 Second Third Three Two * a b c d first five four one six three\n",
      "    two\n",
      "MD: modal auxiliary\n",
      "    can cannot could couldn't dare may might must need ought shall should\n",
      "    shouldn't will would\n",
      "NN: noun, common, singular or mass\n",
      "    common-carrier cabbage knuckle-duster Casino afghan shed thermostat\n",
      "    investment slide humour falloff slick wind hyena override subhumanity\n",
      "    machinist ...\n",
      "NNP: noun, proper, singular\n",
      "    Motown Venneboerger Czestochwa Ranzer Conchita Trumplane Christos\n",
      "    Oceanside Escobar Kreisler Sawyer Cougar Yvette Ervin ODI Darryl CTCA\n",
      "    Shannon A.K.C. Meltex Liverpool ...\n",
      "NNPS: noun, proper, plural\n",
      "    Americans Americas Amharas Amityvilles Amusements Anarcho-Syndicalists\n",
      "    Andalusians Andes Andruses Angels Animals Anthony Antilles Antiques\n",
      "    Apache Apaches Apocrypha ...\n",
      "NNS: noun, common, plural\n",
      "    undergraduates scotches bric-a-brac products bodyguards facets coasts\n",
      "    divestitures storehouses designs clubs fragrances averages\n",
      "    subjectivists apprehensions muses factory-jobs ...\n",
      "PDT: pre-determiner\n",
      "    all both half many quite such sure this\n",
      "POS: genitive marker\n",
      "    ' 's\n",
      "PRP: pronoun, personal\n",
      "    hers herself him himself hisself it itself me myself one oneself ours\n",
      "    ourselves ownself self she thee theirs them themselves they thou thy us\n",
      "PRP$: pronoun, possessive\n",
      "    her his mine my our ours their thy your\n",
      "RB: adverb\n",
      "    occasionally unabatingly maddeningly adventurously professedly\n",
      "    stirringly prominently technologically magisterially predominately\n",
      "    swiftly fiscally pitilessly ...\n",
      "RBR: adverb, comparative\n",
      "    further gloomier grander graver greater grimmer harder harsher\n",
      "    healthier heavier higher however larger later leaner lengthier less-\n",
      "    perfectly lesser lonelier longer louder lower more ...\n",
      "RBS: adverb, superlative\n",
      "    best biggest bluntest earliest farthest first furthest hardest\n",
      "    heartiest highest largest least less most nearest second tightest worst\n",
      "RP: particle\n",
      "    aboard about across along apart around aside at away back before behind\n",
      "    by crop down ever fast for forth from go high i.e. in into just later\n",
      "    low more off on open out over per pie raising start teeth that through\n",
      "    under unto up up-pp upon whole with you\n",
      "SYM: symbol\n",
      "    % & ' '' ''. ) ). * + ,. < = > @ A[fj] U.S U.S.S.R * ** ***\n",
      "TO: \"to\" as preposition or infinitive marker\n",
      "    to\n",
      "UH: interjection\n",
      "    Goodbye Goody Gosh Wow Jeepers Jee-sus Hubba Hey Kee-reist Oops amen\n",
      "    huh howdy uh dammit whammo shucks heck anyways whodunnit honey golly\n",
      "    man baby diddle hush sonuvabitch ...\n",
      "VB: verb, base form\n",
      "    ask assemble assess assign assume atone attention avoid bake balkanize\n",
      "    bank begin behold believe bend benefit bevel beware bless boil bomb\n",
      "    boost brace break bring broil brush build ...\n",
      "VBD: verb, past tense\n",
      "    dipped pleaded swiped regummed soaked tidied convened halted registered\n",
      "    cushioned exacted snubbed strode aimed adopted belied figgered\n",
      "    speculated wore appreciated contemplated ...\n",
      "VBG: verb, present participle or gerund\n",
      "    telegraphing stirring focusing angering judging stalling lactating\n",
      "    hankerin' alleging veering capping approaching traveling besieging\n",
      "    encrypting interrupting erasing wincing ...\n",
      "VBN: verb, past participle\n",
      "    multihulled dilapidated aerosolized chaired languished panelized used\n",
      "    experimented flourished imitated reunifed factored condensed sheared\n",
      "    unsettled primed dubbed desired ...\n",
      "VBP: verb, present tense, not 3rd person singular\n",
      "    predominate wrap resort sue twist spill cure lengthen brush terminate\n",
      "    appear tend stray glisten obtain comprise detest tease attract\n",
      "    emphasize mold postpone sever return wag ...\n",
      "VBZ: verb, present tense, 3rd person singular\n",
      "    bases reconstructs marks mixes displeases seals carps weaves snatches\n",
      "    slumps stretches authorizes smolders pictures emerges stockpiles\n",
      "    seduces fizzes uses bolsters slaps speaks pleads ...\n",
      "WDT: WH-determiner\n",
      "    that what whatever which whichever\n",
      "WP: WH-pronoun\n",
      "    that what whatever whatsoever which who whom whosoever\n",
      "WP$: WH-pronoun, possessive\n",
      "    whose\n",
      "WRB: Wh-adverb\n",
      "    how however whence whenever where whereby whereever wherein whereof why\n",
      "``: opening quotation mark\n",
      "    ` ``\n"
     ]
    }
   ],
   "source": [
    "upenn_tagset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(: opening parenthesis\n",
      "    (\n",
      "): closing parenthesis\n",
      "    )\n",
      "*: negator\n",
      "    not n't\n",
      ",: comma\n",
      "    ,\n",
      "--: dash\n",
      "    --\n",
      ".: sentence terminator\n",
      "    . ? ; ! :\n",
      ":: colon\n",
      "    :\n",
      "ABL: determiner/pronoun, pre-qualifier\n",
      "    quite such rather\n",
      "ABN: determiner/pronoun, pre-quantifier\n",
      "    all half many nary\n",
      "ABX: determiner/pronoun, double conjunction or pre-quantifier\n",
      "    both\n",
      "AP: determiner/pronoun, post-determiner\n",
      "    many other next more last former little several enough most least only\n",
      "    very few fewer past same Last latter less single plenty 'nough lesser\n",
      "    certain various manye next-to-last particular final previous present\n",
      "    nuf\n",
      "AP$: determiner/pronoun, post-determiner, genitive\n",
      "    other's\n",
      "AP+AP: determiner/pronoun, post-determiner, hyphenated pair\n",
      "    many-much\n",
      "AT: article\n",
      "    the an no a every th' ever' ye\n",
      "BE: verb 'to be', infinitive or imperative\n",
      "    be\n",
      "BED: verb 'to be', past tense, 2nd person singular or all persons plural\n",
      "    were\n",
      "BED*: verb 'to be', past tense, 2nd person singular or all persons plural, negated\n",
      "    weren't\n",
      "BEDZ: verb 'to be', past tense, 1st and 3rd person singular\n",
      "    was\n",
      "BEDZ*: verb 'to be', past tense, 1st and 3rd person singular, negated\n",
      "    wasn't\n",
      "BEG: verb 'to be', present participle or gerund\n",
      "    being\n",
      "BEM: verb 'to be', present tense, 1st person singular\n",
      "    am\n",
      "BEM*: verb 'to be', present tense, 1st person singular, negated\n",
      "    ain't\n",
      "BEN: verb 'to be', past participle\n",
      "    been\n",
      "BER: verb 'to be', present tense, 2nd person singular or all persons plural\n",
      "    are art\n",
      "BER*: verb 'to be', present tense, 2nd person singular or all persons plural, negated\n",
      "    aren't ain't\n",
      "BEZ: verb 'to be', present tense, 3rd person singular\n",
      "    is\n",
      "BEZ*: verb 'to be', present tense, 3rd person singular, negated\n",
      "    isn't ain't\n",
      "CC: conjunction, coordinating\n",
      "    and or but plus & either neither nor yet 'n' and/or minus an'\n",
      "CD: numeral, cardinal\n",
      "    two one 1 four 2 1913 71 74 637 1937 8 five three million 87-31 29-5\n",
      "    seven 1,119 fifty-three 7.5 billion hundred 125,000 1,700 60 100 six\n",
      "    ...\n",
      "CD$: numeral, cardinal, genitive\n",
      "    1960's 1961's .404's\n",
      "CS: conjunction, subordinating\n",
      "    that as after whether before while like because if since for than altho\n",
      "    until so unless though providing once lest s'posin' till whereas\n",
      "    whereupon supposing tho' albeit then so's 'fore\n",
      "DO: verb 'to do', uninflected present tense, infinitive or imperative\n",
      "    do dost\n",
      "DO*: verb 'to do', uninflected present tense or imperative, negated\n",
      "    don't\n",
      "DO+PPSS: verb 'to do', past or present tense + pronoun, personal, nominative, not 3rd person singular\n",
      "    d'you\n",
      "DOD: verb 'to do', past tense\n",
      "    did done\n",
      "DOD*: verb 'to do', past tense, negated\n",
      "    didn't\n",
      "DOZ: verb 'to do', present tense, 3rd person singular\n",
      "    does\n",
      "DOZ*: verb 'to do', present tense, 3rd person singular, negated\n",
      "    doesn't don't\n",
      "DT: determiner/pronoun, singular\n",
      "    this each another that 'nother\n",
      "DT$: determiner/pronoun, singular, genitive\n",
      "    another's\n",
      "DT+BEZ: determiner/pronoun + verb 'to be', present tense, 3rd person singular\n",
      "    that's\n",
      "DT+MD: determiner/pronoun + modal auxillary\n",
      "    that'll this'll\n",
      "DTI: determiner/pronoun, singular or plural\n",
      "    any some\n",
      "DTS: determiner/pronoun, plural\n",
      "    these those them\n",
      "DTS+BEZ: pronoun, plural + verb 'to be', present tense, 3rd person singular\n",
      "    them's\n",
      "DTX: determiner, pronoun or double conjunction\n",
      "    neither either one\n",
      "EX: existential there\n",
      "    there\n",
      "EX+BEZ: existential there + verb 'to be', present tense, 3rd person singular\n",
      "    there's\n",
      "EX+HVD: existential there + verb 'to have', past tense\n",
      "    there'd\n",
      "EX+HVZ: existential there + verb 'to have', present tense, 3rd person singular\n",
      "    there's\n",
      "EX+MD: existential there + modal auxillary\n",
      "    there'll there'd\n",
      "FW-*: foreign word: negator\n",
      "    pas non ne\n",
      "FW-AT: foreign word: article\n",
      "    la le el un die der ein keine eine das las les Il\n",
      "FW-AT+NN: foreign word: article + noun, singular, common\n",
      "    l'orchestre l'identite l'arcade l'ange l'assistance l'activite\n",
      "    L'Universite l'independance L'Union L'Unita l'osservatore\n",
      "FW-AT+NP: foreign word: article + noun, singular, proper\n",
      "    L'Astree L'Imperiale\n",
      "FW-BE: foreign word: verb 'to be', infinitive or imperative\n",
      "    sit\n",
      "FW-BER: foreign word: verb 'to be', present tense, 2nd person singular or all persons plural\n",
      "    sind sunt etes\n",
      "FW-BEZ: foreign word: verb 'to be', present tense, 3rd person singular\n",
      "    ist est\n",
      "FW-CC: foreign word: conjunction, coordinating\n",
      "    et ma mais und aber och nec y\n",
      "FW-CD: foreign word: numeral, cardinal\n",
      "    une cinq deux sieben unam zwei\n",
      "FW-CS: foreign word: conjunction, subordinating\n",
      "    bevor quam ma\n",
      "FW-DT: foreign word: determiner/pronoun, singular\n",
      "    hoc\n",
      "FW-DT+BEZ: foreign word: determiner + verb 'to be', present tense, 3rd person singular\n",
      "    c'est\n",
      "FW-DTS: foreign word: determiner/pronoun, plural\n",
      "    haec\n",
      "FW-HV: foreign word: verb 'to have', present tense, not 3rd person singular\n",
      "    habe\n",
      "FW-IN: foreign word: preposition\n",
      "    ad de en a par con dans ex von auf super post sine sur sub avec per\n",
      "    inter sans pour pendant in di\n",
      "FW-IN+AT: foreign word: preposition + article\n",
      "    della des du aux zur d'un del dell'\n",
      "FW-IN+NN: foreign word: preposition + noun, singular, common\n",
      "    d'etat d'hotel d'argent d'identite d'art\n",
      "FW-IN+NP: foreign word: preposition + noun, singular, proper\n",
      "    d'Yquem d'Eiffel\n",
      "FW-JJ: foreign word: adjective\n",
      "    avant Espagnol sinfonica Siciliana Philharmonique grand publique haute\n",
      "    noire bouffe Douce meme humaine bel serieuses royaux anticus presto\n",
      "    Sovietskaya Bayerische comique schwarzen ...\n",
      "FW-JJR: foreign word: adjective, comparative\n",
      "    fortiori\n",
      "FW-JJT: foreign word: adjective, superlative\n",
      "    optimo\n",
      "FW-NN: foreign word: noun, singular, common\n",
      "    ballet esprit ersatz mano chatte goutte sang Fledermaus oud def kolkhoz\n",
      "    roi troika canto boite blutwurst carne muzyka bonheur monde piece force\n",
      "    ...\n",
      "FW-NN$: foreign word: noun, singular, common, genitive\n",
      "    corporis intellectus arte's dei aeternitatis senioritatis curiae\n",
      "    patronne's chambre's\n",
      "FW-NNS: foreign word: noun, plural, common\n",
      "    al culpas vopos boites haflis kolkhozes augen tyrannis alpha-beta-\n",
      "    gammas metis banditos rata phis negociants crus Einsatzkommandos\n",
      "    kamikaze wohaws sabinas zorrillas palazzi engages coureurs corroborees\n",
      "    yori Ubermenschen ...\n",
      "FW-NP: foreign word: noun, singular, proper\n",
      "    Karshilama Dieu Rundfunk Afrique Espanol Afrika Spagna Gott Carthago\n",
      "    deus\n",
      "FW-NPS: foreign word: noun, plural, proper\n",
      "    Svenskarna Atlantes Dieux\n",
      "FW-NR: foreign word: noun, singular, adverbial\n",
      "    heute morgen aujourd'hui hoy\n",
      "FW-OD: foreign word: numeral, ordinal\n",
      "    18e 17e quintus\n",
      "FW-PN: foreign word: pronoun, nominal\n",
      "    hoc\n",
      "FW-PP$: foreign word: determiner, possessive\n",
      "    mea mon deras vos\n",
      "FW-PPL: foreign word: pronoun, singular, reflexive\n",
      "    se\n",
      "FW-PPL+VBZ: foreign word: pronoun, singular, reflexive + verb, present tense, 3rd person singular\n",
      "    s'excuse s'accuse\n",
      "FW-PPO: pronoun, personal, accusative\n",
      "    lui me moi mi\n",
      "FW-PPO+IN: foreign word: pronoun, personal, accusative + preposition\n",
      "    mecum tecum\n",
      "FW-PPS: foreign word: pronoun, personal, nominative, 3rd person singular\n",
      "    il\n",
      "FW-PPSS: foreign word: pronoun, personal, nominative, not 3rd person singular\n",
      "    ich vous sie je\n",
      "FW-PPSS+HV: foreign word: pronoun, personal, nominative, not 3rd person singular + verb 'to have', present tense, not 3rd person singular\n",
      "    j'ai\n",
      "FW-QL: foreign word: qualifier\n",
      "    minus\n",
      "FW-RB: foreign word: adverb\n",
      "    bas assai deja um wiederum cito velociter vielleicht simpliciter non zu\n",
      "    domi nuper sic forsan olim oui semper tout despues hors\n",
      "FW-RB+CC: foreign word: adverb + conjunction, coordinating\n",
      "    forisque\n",
      "FW-TO+VB: foreign word: infinitival to + verb, infinitive\n",
      "    d'entretenir\n",
      "FW-UH: foreign word: interjection\n",
      "    sayonara bien adieu arigato bonjour adios bueno tchalo ciao o\n",
      "FW-VB: foreign word: verb, present tense, not 3rd person singular, imperative or infinitive\n",
      "    nolo contendere vive fermate faciunt esse vade noli tangere dites duces\n",
      "    meminisse iuvabit gosaimasu voulez habla ksu'u'peli'afo lacheln miuchi\n",
      "    say allons strafe portant\n",
      "FW-VBD: foreign word: verb, past tense\n",
      "    stabat peccavi audivi\n",
      "FW-VBG: foreign word: verb, present participle or gerund\n",
      "    nolens volens appellant seq. obliterans servanda dicendi delenda\n",
      "FW-VBN: foreign word: verb, past participle\n",
      "    vue verstrichen rasa verboten engages\n",
      "FW-VBZ: foreign word: verb, present tense, 3rd person singular\n",
      "    gouverne sinkt sigue diapiace\n",
      "FW-WDT: foreign word: WH-determiner\n",
      "    quo qua quod que quok\n",
      "FW-WPO: foreign word: WH-pronoun, accusative\n",
      "    quibusdam\n",
      "FW-WPS: foreign word: WH-pronoun, nominative\n",
      "    qui\n",
      "HV: verb 'to have', uninflected present tense, infinitive or imperative\n",
      "    have hast\n",
      "HV*: verb 'to have', uninflected present tense or imperative, negated\n",
      "    haven't ain't\n",
      "HV+TO: verb 'to have', uninflected present tense + infinitival to\n",
      "    hafta\n",
      "HVD: verb 'to have', past tense\n",
      "    had\n",
      "HVD*: verb 'to have', past tense, negated\n",
      "    hadn't\n",
      "HVG: verb 'to have', present participle or gerund\n",
      "    having\n",
      "HVN: verb 'to have', past participle\n",
      "    had\n",
      "HVZ: verb 'to have', present tense, 3rd person singular\n",
      "    has hath\n",
      "HVZ*: verb 'to have', present tense, 3rd person singular, negated\n",
      "    hasn't ain't\n",
      "IN: preposition\n",
      "    of in for by considering to on among at through with under into\n",
      "    regarding than since despite according per before toward against as\n",
      "    after during including between without except upon out over ...\n",
      "IN+IN: preposition, hyphenated pair\n",
      "    f'ovuh\n",
      "IN+PPO: preposition + pronoun, personal, accusative\n",
      "    t'hi-im\n",
      "JJ: adjective\n",
      "    ecent over-all possible hard-fought favorable hard meager fit such\n",
      "    widespread outmoded inadequate ambiguous grand clerical effective\n",
      "    orderly federal foster general proportionate ...\n",
      "JJ$: adjective, genitive\n",
      "    Great's\n",
      "JJ+JJ: adjective, hyphenated pair\n",
      "    big-large long-far\n",
      "JJR: adjective, comparative\n",
      "    greater older further earlier later freer franker wider better deeper\n",
      "    firmer tougher faster higher bigger worse younger lighter nicer slower\n",
      "    happier frothier Greater newer Elder ...\n",
      "JJR+CS: adjective + conjunction, coordinating\n",
      "    lighter'n\n",
      "JJS: adjective, semantically superlative\n",
      "    top chief principal northernmost master key head main tops utmost\n",
      "    innermost foremost uppermost paramount topmost\n",
      "JJT: adjective, superlative\n",
      "    best largest coolest calmest latest greatest earliest simplest\n",
      "    strongest newest fiercest unhappiest worst youngest worthiest fastest\n",
      "    hottest fittest lowest finest smallest staunchest ...\n",
      "MD: modal auxillary\n",
      "    should may might will would must can could shall ought need wilt\n",
      "MD*: modal auxillary, negated\n",
      "    cannot couldn't wouldn't can't won't shouldn't shan't mustn't musn't\n",
      "MD+HV: modal auxillary + verb 'to have', uninflected form\n",
      "    shouldda musta coulda must've woulda could've\n",
      "MD+PPSS: modal auxillary + pronoun, personal, nominative, not 3rd person singular\n",
      "    willya\n",
      "MD+TO: modal auxillary + infinitival to\n",
      "    oughta\n",
      "NN: noun, singular, common\n",
      "    failure burden court fire appointment awarding compensation Mayor\n",
      "    interim committee fact effect airport management surveillance jail\n",
      "    doctor intern extern night weekend duty legislation Tax Office ...\n",
      "NN$: noun, singular, common, genitive\n",
      "    season's world's player's night's chapter's golf's football's\n",
      "    baseball's club's U.'s coach's bride's bridegroom's board's county's\n",
      "    firm's company's superintendent's mob's Navy's ...\n",
      "NN+BEZ: noun, singular, common + verb 'to be', present tense, 3rd person singular\n",
      "    water's camera's sky's kid's Pa's heat's throat's father's money's\n",
      "    undersecretary's granite's level's wife's fat's Knife's fire's name's\n",
      "    hell's leg's sun's roulette's cane's guy's kind's baseball's ...\n",
      "NN+HVD: noun, singular, common + verb 'to have', past tense\n",
      "    Pa'd\n",
      "NN+HVZ: noun, singular, common + verb 'to have', present tense, 3rd person singular\n",
      "    guy's Knife's boat's summer's rain's company's\n",
      "NN+IN: noun, singular, common + preposition\n",
      "    buncha\n",
      "NN+MD: noun, singular, common + modal auxillary\n",
      "    cowhand'd sun'll\n",
      "NN+NN: noun, singular, common, hyphenated pair\n",
      "    stomach-belly\n",
      "NNS: noun, plural, common\n",
      "    irregularities presentments thanks reports voters laws legislators\n",
      "    years areas adjustments chambers $100 bonds courts sales details raises\n",
      "    sessions members congressmen votes polls calls ...\n",
      "NNS$: noun, plural, common, genitive\n",
      "    taxpayers' children's members' States' women's cutters' motorists'\n",
      "    steelmakers' hours' Nations' lawyers' prisoners' architects' tourists'\n",
      "    Employers' secretaries' Rogues' ...\n",
      "NNS+MD: noun, plural, common + modal auxillary\n",
      "    duds'd oystchers'll\n",
      "NP: noun, singular, proper\n",
      "    Fulton Atlanta September-October Durwood Pye Ivan Allen Jr. Jan.\n",
      "    Alpharetta Grady William B. Hartsfield Pearl Williams Aug. Berry J. M.\n",
      "    Cheshire Griffin Opelika Ala. E. Pelham Snodgrass ...\n",
      "NP$: noun, singular, proper, genitive\n",
      "    Green's Landis' Smith's Carreon's Allison's Boston's Spahn's Willie's\n",
      "    Mickey's Milwaukee's Mays' Howsam's Mantle's Shaw's Wagner's Rickey's\n",
      "    Shea's Palmer's Arnold's Broglio's ...\n",
      "NP+BEZ: noun, singular, proper + verb 'to be', present tense, 3rd person singular\n",
      "    W.'s Ike's Mack's Jack's Kate's Katharine's Black's Arthur's Seaton's\n",
      "    Buckhorn's Breed's Penny's Rob's Kitty's Blackwell's Myra's Wally's\n",
      "    Lucille's Springfield's Arlene's\n",
      "NP+HVZ: noun, singular, proper + verb 'to have', present tense, 3rd person singular\n",
      "    Bill's Guardino's Celie's Skolman's Crosson's Tim's Wally's\n",
      "NP+MD: noun, singular, proper + modal auxillary\n",
      "    Gyp'll John'll\n",
      "NPS: noun, plural, proper\n",
      "    Chases Aderholds Chapelles Armisteads Lockies Carbones French Marskmen\n",
      "    Toppers Franciscans Romans Cadillacs Masons Blacks Catholics British\n",
      "    Dixiecrats Mississippians Congresses ...\n",
      "NPS$: noun, plural, proper, genitive\n",
      "    Republicans' Orioles' Birds' Yanks' Redbirds' Bucs' Yankees' Stevenses'\n",
      "    Geraghtys' Burkes' Wackers' Achaeans' Dresbachs' Russians' Democrats'\n",
      "    Gershwins' Adventists' Negroes' Catholics' ...\n",
      "NR: noun, singular, adverbial\n",
      "    Friday home Wednesday Tuesday Monday Sunday Thursday yesterday tomorrow\n",
      "    tonight West East Saturday west left east downtown north northeast\n",
      "    southeast northwest North South right ...\n",
      "NR$: noun, singular, adverbial, genitive\n",
      "    Saturday's Monday's yesterday's tonight's tomorrow's Sunday's\n",
      "    Wednesday's Friday's today's Tuesday's West's Today's South's\n",
      "NR+MD: noun, singular, adverbial + modal auxillary\n",
      "    today'll\n",
      "NRS: noun, plural, adverbial\n",
      "    Sundays Mondays Saturdays Wednesdays Souths Fridays\n",
      "OD: numeral, ordinal\n",
      "    first 13th third nineteenth 2d 61st second sixth eighth ninth twenty-\n",
      "    first eleventh 50th eighteenth- Thirty-ninth 72nd 1/20th twentieth\n",
      "    mid-19th thousandth 350th sixteenth 701st ...\n",
      "PN: pronoun, nominal\n",
      "    none something everything one anyone nothing nobody everybody everyone\n",
      "    anybody anything someone no-one nothin\n",
      "PN$: pronoun, nominal, genitive\n",
      "    one's someone's anybody's nobody's everybody's anyone's everyone's\n",
      "PN+BEZ: pronoun, nominal + verb 'to be', present tense, 3rd person singular\n",
      "    nothing's everything's somebody's nobody's someone's\n",
      "PN+HVD: pronoun, nominal + verb 'to have', past tense\n",
      "    nobody'd\n",
      "PN+HVZ: pronoun, nominal + verb 'to have', present tense, 3rd person singular\n",
      "    nobody's somebody's one's\n",
      "PN+MD: pronoun, nominal + modal auxillary\n",
      "    someone'll somebody'll anybody'd\n",
      "PP$: determiner, possessive\n",
      "    our its his their my your her out thy mine thine\n",
      "PP$$: pronoun, possessive\n",
      "    ours mine his hers theirs yours\n",
      "PPL: pronoun, singular, reflexive\n",
      "    itself himself myself yourself herself oneself ownself\n",
      "PPLS: pronoun, plural, reflexive\n",
      "    themselves ourselves yourselves\n",
      "PPO: pronoun, personal, accusative\n",
      "    them it him me us you 'em her thee we'uns\n",
      "PPS: pronoun, personal, nominative, 3rd person singular\n",
      "    it he she thee\n",
      "PPS+BEZ: pronoun, personal, nominative, 3rd person singular + verb 'to be', present tense, 3rd person singular\n",
      "    it's he's she's\n",
      "PPS+HVD: pronoun, personal, nominative, 3rd person singular + verb 'to have', past tense\n",
      "    she'd he'd it'd\n",
      "PPS+HVZ: pronoun, personal, nominative, 3rd person singular + verb 'to have', present tense, 3rd person singular\n",
      "    it's he's she's\n",
      "PPS+MD: pronoun, personal, nominative, 3rd person singular + modal auxillary\n",
      "    he'll she'll it'll he'd it'd she'd\n",
      "PPSS: pronoun, personal, nominative, not 3rd person singular\n",
      "    they we I you ye thou you'uns\n",
      "PPSS+BEM: pronoun, personal, nominative, not 3rd person singular + verb 'to be', present tense, 1st person singular\n",
      "    I'm Ahm\n",
      "PPSS+BER: pronoun, personal, nominative, not 3rd person singular + verb 'to be', present tense, 2nd person singular or all persons plural\n",
      "    we're you're they're\n",
      "PPSS+BEZ: pronoun, personal, nominative, not 3rd person singular + verb 'to be', present tense, 3rd person singular\n",
      "    you's\n",
      "PPSS+BEZ*: pronoun, personal, nominative, not 3rd person singular + verb 'to be', present tense, 3rd person singular, negated\n",
      "    'tain't\n",
      "PPSS+HV: pronoun, personal, nominative, not 3rd person singular + verb 'to have', uninflected present tense\n",
      "    I've we've they've you've\n",
      "PPSS+HVD: pronoun, personal, nominative, not 3rd person singular + verb 'to have', past tense\n",
      "    I'd you'd we'd they'd\n",
      "PPSS+MD: pronoun, personal, nominative, not 3rd person singular + modal auxillary\n",
      "    you'll we'll I'll we'd I'd they'll they'd you'd\n",
      "PPSS+VB: pronoun, personal, nominative, not 3rd person singular + verb 'to verb', uninflected present tense\n",
      "    y'know\n",
      "QL: qualifier, pre\n",
      "    well less very most so real as highly fundamentally even how much\n",
      "    remarkably somewhat more completely too thus ill deeply little overly\n",
      "    halfway almost impossibly far severly such ...\n",
      "QLP: qualifier, post\n",
      "    indeed enough still 'nuff\n",
      "RB: adverb\n",
      "    only often generally also nevertheless upon together back newly no\n",
      "    likely meanwhile near then heavily there apparently yet outright fully\n",
      "    aside consistently specifically formally ever just ...\n",
      "RB$: adverb, genitive\n",
      "    else's\n",
      "RB+BEZ: adverb + verb 'to be', present tense, 3rd person singular\n",
      "    here's there's\n",
      "RB+CS: adverb + conjunction, coordinating\n",
      "    well's soon's\n",
      "RBR: adverb, comparative\n",
      "    further earlier better later higher tougher more harder longer sooner\n",
      "    less faster easier louder farther oftener nearer cheaper slower tighter\n",
      "    lower worse heavier quicker ...\n",
      "RBR+CS: adverb, comparative + conjunction, coordinating\n",
      "    more'n\n",
      "RBT: adverb, superlative\n",
      "    most best highest uppermost nearest brightest hardest fastest deepest\n",
      "    farthest loudest ...\n",
      "RN: adverb, nominal\n",
      "    here afar then\n",
      "RP: adverb, particle\n",
      "    up out off down over on in about through across after\n",
      "RP+IN: adverb, particle + preposition\n",
      "    out'n outta\n",
      "TO: infinitival to\n",
      "    to t'\n",
      "TO+VB: infinitival to + verb, infinitive\n",
      "    t'jawn t'lah\n",
      "UH: interjection\n",
      "    Hurrah bang whee hmpf ah goodbye oops oh-the-pain-of-it ha crunch say\n",
      "    oh why see well hello lo alas tarantara rum-tum-tum gosh hell keerist\n",
      "    Jesus Keeeerist boy c'mon 'mon goddamn bah hoo-pig damn ...\n",
      "VB: verb, base: uninflected present, imperative or infinitive\n",
      "    investigate find act follow inure achieve reduce take remedy re-set\n",
      "    distribute realize disable feel receive continue place protect\n",
      "    eliminate elaborate work permit run enter force ...\n",
      "VB+AT: verb, base: uninflected present or infinitive + article\n",
      "    wanna\n",
      "VB+IN: verb, base: uninflected present, imperative or infinitive + preposition\n",
      "    lookit\n",
      "VB+JJ: verb, base: uninflected present, imperative or infinitive + adjective\n",
      "    die-dead\n",
      "VB+PPO: verb, uninflected present tense + pronoun, personal, accusative\n",
      "    let's lemme gimme\n",
      "VB+RP: verb, imperative + adverbial particle\n",
      "    g'ahn c'mon\n",
      "VB+TO: verb, base: uninflected present, imperative or infinitive + infinitival to\n",
      "    wanta wanna\n",
      "VB+VB: verb, base: uninflected present, imperative or infinitive; hypenated pair\n",
      "    say-speak\n",
      "VBD: verb, past tense\n",
      "    said produced took recommended commented urged found added praised\n",
      "    charged listed became announced brought attended wanted voted defeated\n",
      "    received got stood shot scheduled feared promised made ...\n",
      "VBG: verb, present participle or gerund\n",
      "    modernizing improving purchasing Purchasing lacking enabling pricing\n",
      "    keeping getting picking entering voting warning making strengthening\n",
      "    setting neighboring attending participating moving ...\n",
      "VBG+TO: verb, present participle + infinitival to\n",
      "    gonna\n",
      "VBN: verb, past participle\n",
      "    conducted charged won received studied revised operated accepted\n",
      "    combined experienced recommended effected granted seen protected\n",
      "    adopted retarded notarized selected composed gotten printed ...\n",
      "VBN+TO: verb, past participle + infinitival to\n",
      "    gotta\n",
      "VBZ: verb, present tense, 3rd person singular\n",
      "    deserves believes receives takes goes expires says opposes starts\n",
      "    permits expects thinks faces votes teaches holds calls fears spends\n",
      "    collects backs eliminates sets flies gives seeks reads ...\n",
      "WDT: WH-determiner\n",
      "    which what whatever whichever whichever-the-hell\n",
      "WDT+BER: WH-determiner + verb 'to be', present tense, 2nd person singular or all persons plural\n",
      "    what're\n",
      "WDT+BER+PP: WH-determiner + verb 'to be', present, 2nd person singular or all persons plural + pronoun, personal, nominative, not 3rd person singular\n",
      "    whaddya\n",
      "WDT+BEZ: WH-determiner + verb 'to be', present tense, 3rd person singular\n",
      "    what's\n",
      "WDT+DO+PPS: WH-determiner + verb 'to do', uninflected present tense + pronoun, personal, nominative, not 3rd person singular\n",
      "    whaddya\n",
      "WDT+DOD: WH-determiner + verb 'to do', past tense\n",
      "    what'd\n",
      "WDT+HVZ: WH-determiner + verb 'to have', present tense, 3rd person singular\n",
      "    what's\n",
      "WP$: WH-pronoun, genitive\n",
      "    whose whosever\n",
      "WPO: WH-pronoun, accusative\n",
      "    whom that who\n",
      "WPS: WH-pronoun, nominative\n",
      "    that who whoever whosoever what whatsoever\n",
      "WPS+BEZ: WH-pronoun, nominative + verb 'to be', present, 3rd person singular\n",
      "    that's who's\n",
      "WPS+HVD: WH-pronoun, nominative + verb 'to have', past tense\n",
      "    who'd\n",
      "WPS+HVZ: WH-pronoun, nominative + verb 'to have', present tense, 3rd person singular\n",
      "    who's that's\n",
      "WPS+MD: WH-pronoun, nominative + modal auxillary\n",
      "    who'll that'd who'd that'll\n",
      "WQL: WH-qualifier\n",
      "    however how\n",
      "WRB: WH-adverb\n",
      "    however when where why whereby wherever how whenever whereon wherein\n",
      "    wherewith wheare wherefore whereof howsabout\n",
      "WRB+BER: WH-adverb + verb 'to be', present, 2nd person singular or all persons plural\n",
      "    where're\n",
      "WRB+BEZ: WH-adverb + verb 'to be', present, 3rd person singular\n",
      "    how's where's\n",
      "WRB+DO: WH-adverb + verb 'to do', present, not 3rd person singular\n",
      "    howda\n",
      "WRB+DOD: WH-adverb + verb 'to do', past tense\n",
      "    where'd how'd\n",
      "WRB+DOD*: WH-adverb + verb 'to do', past tense, negated\n",
      "    whyn't\n",
      "WRB+DOZ: WH-adverb + verb 'to do', present tense, 3rd person singular\n",
      "    how's\n",
      "WRB+IN: WH-adverb + preposition\n",
      "    why'n\n",
      "WRB+MD: WH-adverb + modal auxillary\n",
      "    where'd\n"
     ]
    }
   ],
   "source": [
    "brown_tagset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NN: noun, singular, common\n",
      "    failure burden court fire appointment awarding compensation Mayor\n",
      "    interim committee fact effect airport management surveillance jail\n",
      "    doctor intern extern night weekend duty legislation Tax Office ...\n",
      "NN$: noun, singular, common, genitive\n",
      "    season's world's player's night's chapter's golf's football's\n",
      "    baseball's club's U.'s coach's bride's bridegroom's board's county's\n",
      "    firm's company's superintendent's mob's Navy's ...\n",
      "NN+BEZ: noun, singular, common + verb 'to be', present tense, 3rd person singular\n",
      "    water's camera's sky's kid's Pa's heat's throat's father's money's\n",
      "    undersecretary's granite's level's wife's fat's Knife's fire's name's\n",
      "    hell's leg's sun's roulette's cane's guy's kind's baseball's ...\n",
      "NN+HVD: noun, singular, common + verb 'to have', past tense\n",
      "    Pa'd\n",
      "NN+HVZ: noun, singular, common + verb 'to have', present tense, 3rd person singular\n",
      "    guy's Knife's boat's summer's rain's company's\n",
      "NN+IN: noun, singular, common + preposition\n",
      "    buncha\n",
      "NN+MD: noun, singular, common + modal auxillary\n",
      "    cowhand'd sun'll\n",
      "NN+NN: noun, singular, common, hyphenated pair\n",
      "    stomach-belly\n",
      "NNS: noun, plural, common\n",
      "    irregularities presentments thanks reports voters laws legislators\n",
      "    years areas adjustments chambers $100 bonds courts sales details raises\n",
      "    sessions members congressmen votes polls calls ...\n",
      "NNS$: noun, plural, common, genitive\n",
      "    taxpayers' children's members' States' women's cutters' motorists'\n",
      "    steelmakers' hours' Nations' lawyers' prisoners' architects' tourists'\n",
      "    Employers' secretaries' Rogues' ...\n",
      "NNS+MD: noun, plural, common + modal auxillary\n",
      "    duds'd oystchers'll\n",
      "NP: noun, singular, proper\n",
      "    Fulton Atlanta September-October Durwood Pye Ivan Allen Jr. Jan.\n",
      "    Alpharetta Grady William B. Hartsfield Pearl Williams Aug. Berry J. M.\n",
      "    Cheshire Griffin Opelika Ala. E. Pelham Snodgrass ...\n",
      "NP$: noun, singular, proper, genitive\n",
      "    Green's Landis' Smith's Carreon's Allison's Boston's Spahn's Willie's\n",
      "    Mickey's Milwaukee's Mays' Howsam's Mantle's Shaw's Wagner's Rickey's\n",
      "    Shea's Palmer's Arnold's Broglio's ...\n",
      "NP+BEZ: noun, singular, proper + verb 'to be', present tense, 3rd person singular\n",
      "    W.'s Ike's Mack's Jack's Kate's Katharine's Black's Arthur's Seaton's\n",
      "    Buckhorn's Breed's Penny's Rob's Kitty's Blackwell's Myra's Wally's\n",
      "    Lucille's Springfield's Arlene's\n",
      "NP+HVZ: noun, singular, proper + verb 'to have', present tense, 3rd person singular\n",
      "    Bill's Guardino's Celie's Skolman's Crosson's Tim's Wally's\n",
      "NP+MD: noun, singular, proper + modal auxillary\n",
      "    Gyp'll John'll\n",
      "NPS: noun, plural, proper\n",
      "    Chases Aderholds Chapelles Armisteads Lockies Carbones French Marskmen\n",
      "    Toppers Franciscans Romans Cadillacs Masons Blacks Catholics British\n",
      "    Dixiecrats Mississippians Congresses ...\n",
      "NPS$: noun, plural, proper, genitive\n",
      "    Republicans' Orioles' Birds' Yanks' Redbirds' Bucs' Yankees' Stevenses'\n",
      "    Geraghtys' Burkes' Wackers' Achaeans' Dresbachs' Russians' Democrats'\n",
      "    Gershwins' Adventists' Negroes' Catholics' ...\n",
      "NR: noun, singular, adverbial\n",
      "    Friday home Wednesday Tuesday Monday Sunday Thursday yesterday tomorrow\n",
      "    tonight West East Saturday west left east downtown north northeast\n",
      "    southeast northwest North South right ...\n",
      "NR$: noun, singular, adverbial, genitive\n",
      "    Saturday's Monday's yesterday's tonight's tomorrow's Sunday's\n",
      "    Wednesday's Friday's today's Tuesday's West's Today's South's\n",
      "NR+MD: noun, singular, adverbial + modal auxillary\n",
      "    today'll\n",
      "NRS: noun, plural, adverbial\n",
      "    Sundays Mondays Saturdays Wednesdays Souths Fridays\n"
     ]
    }
   ],
   "source": [
    "brown_tagset(\"N.*\")   # 패턴을 지정해서 특정한 태그만 검색해볼 수 있음"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- tag set 마다 품사의 의미를 다르게 표현함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('she', 'UNK'),\n",
       " ('sells', 'UNK'),\n",
       " ('seashells', 'UNK'),\n",
       " ('on', 'UNK'),\n",
       " ('the', 'UNK'),\n",
       " ('seashore', 'UNK')]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_tag(result, \"brown_tagset\")    # 태스 셋을 지정할 수 있음(지정하지 않은 경우와 다르게 나타남)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('she', 'UNK'),\n",
       " ('sells', 'UNK'),\n",
       " ('seashells', 'UNK'),\n",
       " ('on', 'UNK'),\n",
       " ('the', 'UNK'),\n",
       " ('seashore', 'UNK')]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_tag(result, \"upenn_tagset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "she\n",
      "sells\n",
      "seashells\n",
      "skipped: on\n",
      "skipped: the\n",
      "seashore\n",
      "['she', 'sells', 'seashells', 'seashore']\n"
     ]
    }
   ],
   "source": [
    "tags = []\n",
    "for term in pos_tag(result):\n",
    "    if term[1] in [\"IN\", \"DT\"]:    # IN: preposition or conjunction, subordinating,  DT: determiner\n",
    "        print(\"skipped:\", term[0])\n",
    "    else:\n",
    "        print(term[0])\n",
    "        tags.append(term[0].lower())\n",
    "        \n",
    "print(tags)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### untag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk import untag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "escape: .\n",
      "['the', 'little', 'yellow', 'dog', 'barked', 'at', 'the', 'persian', 'cat']\n"
     ]
    }
   ],
   "source": [
    "sentence = \"The little yellow dog barked at the Persian cat.\"\n",
    "\n",
    "pattern = re.compile(r\"[{0}]\".format(re.escape(punctuation)))\n",
    "pattern.sub(\"\", sentence)   # 구두점 제거됨\n",
    "\n",
    "tokens = word_tokenize(sentence)\n",
    "\n",
    "result = []\n",
    "\n",
    "for token in tokens:\n",
    "    if pattern.search(token):\n",
    "        print(\"escape:\", token)\n",
    "    else:\n",
    "        result.append(token.lower())\n",
    "\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "tagged = pos_tag(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('the', 'DT'),\n",
       " ('little', 'JJ'),\n",
       " ('yellow', 'JJ'),\n",
       " ('dog', 'NN'),\n",
       " ('barked', 'VBD'),\n",
       " ('at', 'IN'),\n",
       " ('the', 'DT'),\n",
       " ('persian', 'JJ'),\n",
       " ('cat', 'NN')]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tagged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['the', 'little', 'yellow', 'dog', 'barked', 'at', 'the', 'persian', 'cat']"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "untag(tagged)\n",
    "#[tag[0] for tag in tagged]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FreqDist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- tag set을 dictionary 형태로 처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk import Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([('the', 'DT'),\n",
       "  ('little', 'JJ'),\n",
       "  ('yellow', 'JJ'),\n",
       "  ('dog', 'NN'),\n",
       "  ('barked', 'VBD'),\n",
       "  ('at', 'IN'),\n",
       "  ('the', 'DT'),\n",
       "  ('persian', 'JJ'),\n",
       "  ('cat', 'NN')],\n",
       " ['the/DT',\n",
       "  'little/JJ',\n",
       "  'yellow/JJ',\n",
       "  'dog/NN',\n",
       "  'barked/VBD',\n",
       "  'at/IN',\n",
       "  'the/DT',\n",
       "  'persian/JJ',\n",
       "  'cat/NN'])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tagged, [\"/\".join(tag) for tag in tagged]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FreqDist({('the', 'DT'): 2, ('little', 'JJ'): 1, ('yellow', 'JJ'): 1, ('dog', 'NN'): 1, ('barked', 'VBD'): 1, ('at', 'IN'): 1, ('persian', 'JJ'): 1, ('cat', 'NN'): 1})"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "textObj = Text(tagged)\n",
    "textObj.vocab()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([('the', 'DT'), ('little', 'JJ'), ('yellow', 'JJ'), ('dog', 'NN'), ('barked', 'VBD'), ('at', 'IN'), ('persian', 'JJ'), ('cat', 'NN')])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "textObj.vocab().keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 0.2222222222222222)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "textObj.vocab().N(), textObj.vocab().freq(('the', 'DT'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skipped: ('the', 'DT')\n"
     ]
    }
   ],
   "source": [
    "# 고빈도 단어 skip\n",
    "for key in textObj.vocab().keys():\n",
    "    if textObj.vocab().freq(key) > 0.2:    # frequency는 비율\n",
    "        print(\"skipped:\", key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nltk.probability.FreqDist"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(textObj.vocab())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.probability import FreqDist   # 단어 빈도 관련 기능"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(FreqDist({('the', 'DT'): 2}), 2)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taggedTerms = FreqDist()\n",
    "\n",
    "for key in textObj.vocab().keys():\n",
    "    if key[1].startswith(\"DT\"):\n",
    "        taggedTerms[key] = textObj.vocab().get(key)\n",
    "\n",
    "taggedTerms, taggedTerms.N()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 아래 두 코드는 동일함 (FreqDist에는 일부 메서드가 추가로 제공됨) : key, value 쌍으로 이루어진 dictionary\n",
    "\n",
    "~~~\n",
    "from nltk.probability import FreqDist\n",
    "taggedTerms = FreqDist()\n",
    "~~~\n",
    "\n",
    "~~~\n",
    "taggedTerms = defaultdict(int)\n",
    "from collections import defaultdict\n",
    "~~~"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### gutenberg(대량의 말뭉치)로 POS tagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import gutenberg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "191785"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus = gutenberg.open(gutenberg.fileids()[0]).read()\n",
    "tokens = word_tokenize(corpus)\n",
    "len(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "158270"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus = pattern.sub(\"\", corpus)\n",
    "tokens = word_tokenize(corpus)\n",
    "len(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(158270, 158270, ('Emma', 'NN'))"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tagged = pos_tag(tokens)\n",
    "len(tokens), len(tagged), tagged[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "wordList = [pair[0] for pair in tagged]\n",
    "posList = [pair[1] for pair in tagged]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(158270, 158270, 158270, 158270)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tokens), len(tagged), len(wordList), len(posList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "freqTagged = FreqDist()\n",
    "for row in tagged:    # tuple(단어, 품사)\n",
    "    freqTagged[row] += 1\n",
    "\n",
    "freqWord = FreqDist()\n",
    "for row in wordList:    # 단어\n",
    "    freqWord[row] += 1\n",
    "    \n",
    "freqPos = FreqDist()\n",
    "for row in posList:    # 품사\n",
    "    freqPos[row] += 1\n",
    "    \n",
    "freqTokens = FreqDist()\n",
    "for row in tokens:    # 품사\n",
    "    freqTokens[row] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('to', 'TO'), 5099),\n",
       " (('the', 'DT'), 4822),\n",
       " (('and', 'CC'), 4412),\n",
       " (('of', 'IN'), 4264),\n",
       " (('I', 'PRP'), 2968),\n",
       " (('a', 'DT'), 2962),\n",
       " (('was', 'VBD'), 2369),\n",
       " (('not', 'RB'), 2227),\n",
       " (('in', 'IN'), 2086),\n",
       " (('it', 'PRP'), 2041)]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freqTagged.most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('to', 5099),\n",
       " ('the', 4822),\n",
       " ('and', 4412),\n",
       " ('of', 4264),\n",
       " ('I', 2968),\n",
       " ('a', 2962),\n",
       " ('was', 2369),\n",
       " ('her', 2333),\n",
       " ('not', 2227),\n",
       " ('in', 2086)]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freqWord.most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('NN', 18524),\n",
       " ('IN', 17875),\n",
       " ('PRP', 14866),\n",
       " ('RB', 12479),\n",
       " ('DT', 12276),\n",
       " ('JJ', 10305),\n",
       " ('NNP', 9863),\n",
       " ('VB', 9022),\n",
       " ('VBD', 8948),\n",
       " ('CC', 6445)]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freqPos.most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '빈도')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/SyBQfwJfRGo3BUZt5vPB88/Td8hpTJz8BEujMyh/ZDkzBq+kcUE/pu4cTtub/sTaLTvdrlREGgAFRm3n9cKzz8KIEfD3v8MttzCgexZbH/2Y33vHsT7xTdo/3INJH2jtKRGJLAVGXeDxwFNPwciR8MgjcMMN+DyGD++6jWf6fgPAVd+fyO/HjtONcRGJGAVGXeHxwPjxcMMN8MQTcO21EAhw5Wl9WHnLXFrvOo+PK+6g2ejT2Jizy+1qRaQeUmDUJcbAP/8Jt94KEyfC8OFQUUFm02TWPPIylyZPIjf5czqO/Z3ua4hI2Ckw6hpjYNw4+NvfYPJkGDYM/H48HsPUG4ZzS9brFCTOpss/BrFyY67b1YpIPaLAqIuMgfvugwcegBdfhIsugvJyAB68/I/c0/F/FCUspNvDA1m8bpvLxYpIfaHAqMvuuAMefRTeeAPOOw9KSwG4d+iZjOv+DiWNltPjXwOYu3KTy4WKSH2gwKjrRo2Cf/8bpk+HwYOhuBiA284/lcd6v09p3FqOm3ASPy7d4HKhIlLXKTDqg5Ej4Zln4KOPYMAAeO452LaNGwYPYOIJH1Mes4W+z/wfg//xGF/MW+V2tSJSRykw6osrr3TuZ2zcCH/+M2RkQP/+XLXke57v+iJRFSm8XTqKgdPaETvqKE68526mfpqtpUVEJGQRW3zQGPMMEABSgbettS8YYwYBNwKFwAZr7ajgudVqP5D6tvjgIbEW5syBt9+GadNg/nynvX17vkjN5PHGCcxotYG8lnPBE8Cb34LTC4bw4oPjSEpJcLd2EXFFqIsPRny1WmOMB/gKOBH4FDjDWltqjBkLfBlsC7ndWvvJgT5LgbEfq1c79ze++grKypxACQRY7vfxcFQcbzZdTW7bH/HtyOT6uKt5ZOyteLy68BRpSGrTarXRQA7QAVhkrS0Ntk8DBh5Cu1RHVhb89a/w5pvwzjvw7rvw/vsc+fF0Jr33KjmTZ/F3HsNXHstjsXeQfG1PJk14ye2qRaQWqonAGAM8BKQBlWeS5Qbbqtu+F2PMCGNMtjEme9s2zTmoNmO4/Z4byHt4HsO23ERR4iau2jaUzGGD+PGr2W5XJyK1SEQDwxhzIzDHWjsT5yojtdLLqcG26rbvxVo7yVrb21rbu0mTJmHuQcMRHR/L8xMe5pe/zOWk1ZexvtV3nPjqxezcriVGRMQRscAwxlwD5FtrXw42rQC6GWNigs8H49yrqG67RFBGZnO+eH4K98eOo7TpMgaOutLtkkSklohIYBhj+gG3A32NMc8aY57FGU4aA7xijHkBiAE+ttZWVKc9EvXKb9119184esUQ5rZ7kwfuH+92OSJSC2hPbzmg3K25tBjTh/LYPBYO/ZLOPTq5XZKIREBt+paU1FGpTVN5oc8jBOJ2cvLjIwhocyaRBk2BIQd13iVnc87mK9mc9TWXXX+L2+WIiIsUGFKlNyY8Tuq643gxdQIfvPWp2+WIiEsUGFIlX5SPT/7f05jyWM775AYK8grcLklEXKDAkJD07HcMN3tvoSjjZ3r+5SJ+Xr3Z7ZJEpIYpMCRkD/79NnquOI/lR7xLt+cyafvXC5jw7jda8VakgVBgSLXMnvoa73kf55jsE1gb/xEjZ59Io5uO5qJHn2Jjzi63yxORCNI8DDk0K1awdejl3Ow3vN5nB8VNf4byOJoVDuKUzDO54YwzObZjK7erFJEQaB6GRFb79jT9ZgZTzhxIwVOLePZ/J3FU/p/I8S7gpfyrOe6V1sTf2IP/+9tdPPvh9/g1h0OkzlNgyKGLioIxY/B89RX/b+ca5j85hVLO5e2TZnO670GibCIzzTiGz+pL7G1t6HXHKP7z0Szd8xCpozQkJeGRlwe33AKTJkG7ds4e4wMHsnJjLg9Pe49py19nS+KH4C3Hu6sNPWLO5/qBF3LJyb3weIzb1Ys0aBqSkpqVnAxPPw0zZoAxcPLJMHw47eI9TLz2UjY/Np01I7dyZfrzpFZ0Jdv7L4bNPJZTx451u3IRCZECQ8JrwABnH/FbboHJk6FLF2d/caBNsxSeGTmMrY+9x4qrt9A67wI+94/h5S/muFuziIREgSHhFxcHDz4IP/wATZvC4MHw0t7bvrZrkcqMm57CU5LOn9++goLiMpeKFZFQKTAkcnr1gh9/hL59YeRI+OWXvV5u1yKV2456mpKUeZz10DiXihSRUCkwJLKiomDKFCgrgyuvhH2+ZPHApWfTNn8oX1SM5bWv5rlUpIiEQoEhkXfkkfDQQ/Dhh863qPbx8Y2P4ylN4/L/XU5RSbkLBYpIKBQYUjOuuQZOOQVGj4ZVq/Z66chWadzSZSLFKXM566F/uFSgiFRFgSE1w+OB554DrxcuvxwqKvZ6edywwbTJv4jP/ffzxtfz3alRRA5KgSE1p3VreOIJ+Ppr+Ne/fvPyRzc8gSltzGVvXaGhKZFaSIEhNeuyy+Ccc+DOO2HRor1e6tg6ndEdJ1Cc8hPtb7+AG599jeUbclwqVET2paVBpOZt2QLdukGbNvDdd843qSrpc9dtzAo8BTH5YA3xeT05qtEgzu1xKsN/fwIpCbEuFS5SP4W6NIgCQ9zx5ptw3nlw9NFw7bVw8cWQkPDryyVlfl74PJuXf/iE7NxPyE/6Drx+KIunRfFpnNnuHG4+50yObJXmYidE6gcFhtR+U6bAo4/CggWQmAiXXAJXXw3du//m1I05u5j4wZe8teB9FgemE0j4BQJeUvJOZEDzcxhx8un07dxGVx8ih0CBIXWDtfD99zBxIrz6KpSWOjPDBw+Gzp2hUyfIygKf79cf8VcEePHz2Tzz9dtkF7xNafLCPe9Xkkx0aXPiA81J9mbQLK41d/5hGGf36eJC50TqBgWG1D25uc5Vx6RJsGTJnvboaGfyX+fO0Ls3XHCBEyJBn89dyUvffM26HRvZXLiJ3LLN5Ac2UezbhD9uA3jLabPrIp6+6B5+37uDCx0Tqd0UGFK37dgBS5fC4sVOeCxe7BwrVjiv9+3r3Pe44AJngcMDWLp+O8OefoRZdjz4SmhXeCmTLrmbk49pV0MdEan9FBhSP61ZA6+8Ai+/7Cyj7vXCoEFOcPTv72zeZH67IdPPa7YybNKDzPZMAG85HYuu4C8nDeNP/XuRmhRX8/0QqUUUGFL/LVzoBMdLLzlBApCWBn36OFcgffrAscdCUtKvPzJ35SYuf/YfzPNNBF8ZVPiI33UM7WL6cGLbvpzftw/9j8rSLoDSoCgwpOGwFn7+2ZnT8f33zlF5UmB0NDRqBPHxv/66PCGdqS2P4POUAIuil7MzMRuiCwEwRU3JKOtHjyb9+MPR/bjopF769pXUawoMadh27nQ2cPrpJ+dxYSEUFe35NT8f5s1zXgPKMpoz/bhB/C8jjVlRG1kX8xPlScH7JRVRJOzqRbvY4+jSpDPHt+vMKd070aVNU12JSL2gwBCpSiDgXIl8841zzJy5Z2gL+LlJJi92OYbPMnwsabyO/NRFEF306+umpDEJxZ1pGd2VAVn9uep3p3BMu+YudETk8CgwRA7Fhg3OvZHly/c+1qzBH6jgx6TmfJ7VgdlZzVmS6mdj3GbyExZiY50rlei8znSMPoUzO5/C1acNoE2zFJc7JFI1BYZIOJWVOVcj336751i92nkpIZHXh4/mpdRYZuXMICf+a+dKJOClS8lw3r3h72Q1b+xyB0QOzPXAMMZ4gfuA3tba04Jtg4AbgUJgg7V21KG0H4gCQ2rUpk3Ojfbnn4d33nEmFk6YQMHx/Xjuk++ZNPNVFsZOxJSk8efWDzHxmsvwebVAtNQ+oQZGJP/0ngW8B/iCBRngdmCItfYCoMgYc2p12yNYr0j1NG8OQ4bA9OnOUVwMAweSMPzPXH9cBxY8+CSvnDybRqXt+U/OFaSO7q/NoaROi1hgWGunWWu/q9TUAVhkrS0NPp8GDDyEdpHa56yznK/23nUXvP46dOwI//oXF6YYdlz3Xy6Pf5KC2KWc/2lPet5+Iy98NpvP5qzg5zVb2VlQQiBQf4aGpf7yVX1K2KQBuZWe5wbbqtu+F2PMCGAEQGZmZngrFqmO+Hi4/35nk6jrroMbbwScv2TPAXfFJXPWoBOY0/NxLv1mnx0HK6IwZUk0Ke3D61eMp3/3rN+8vYjbajIwcoDUSs9Tg23Vbd+LtXYSMAmcexjhLVnkEBx5JHz4oTMP5JdfnDkf+fm0y89nUX4+72e35JsVK8jt1JHcvr3JKy8i35/PzkAuS+Je5qRXuzHkw3G8Ovo63fOQWqUmA2MF0M0YExMcZhoMfHkI7SK1nzFw/PH7fekMaznjscdg9GjYutK5/5GeDsCsxXdx1qSreCv6r6SOfo1Xh/6H04/tWJOVixxQTfzzpQzAWlsBjAFeMca8AMQAH1e3vQbqFYksY2DUKOdex08/OeteBVfhPb5zazY/+h7D06dQELeIM6YfzekPPEhJmd/lokU0D0PEXTNnwjnnOCEyfboTHkHzV23mjH9fxy/JbxK38xhu6T2Wv110hpYjkbCrDV+rFZGqnHCCMwkwKQlOPhkmTIAtWwDofkQGG/75BqNavU65dyf3rfgDSaOP576X3te3qsQVCgwRt3Xo4Kyw26MHjBwJGRnOzoJ33QXffMOjwwaTN3YZl6U8Q4l3K/cuP5Ok0X0Y8/IHCg6pURqSEqktAgGYOxc++MD5ltV330FFBSQnQ79+kJ5OQWIK1xZW8FLaO1QkrSc2txtJtMZ4fBg8GAzGeDB4iPMk0CgqicToJJJikkiJSyI1PonBxx3LKT3au91bqUVcXxrEDQoMqVd27oRPP3XCY/Zs5/nOnZCXR4HHy9XH9GH60XlU+PxYD1ifAY/BesFSQYW3iIAvHxudD57AXm/deMfJXNp1BPdfPJikRjEudVBqCwWGSH0VCEBBgRMeGzbAjBnwySfOvZDycoiJce6NdO8OWVkE2maxvUlzNiY0Zu2uEp787G1m5D2DP3ENpjidXt7LGfvH4fy+dwe3eyYuUWCINDQFBfDVV85Vyeefw7JlzvpWlTVrBm3b4m+dyYONmjMhegkbMz4Hr5+ovA74bDweYvDZGLzE4DMxxJh4UqKb0jS+GS2SmtE6rRlHNG1G97YtObZjK3f6KmGlwBBp6KyFrVth1SpnKfbVq53Ha9fCunXOUVrK3IQm3HpMF35uXoo/KoA/CvzRASp8FVR4y/FHlVAesw1i837zEbF5R3FS6sXcNfhP/F+3tjXfRwkLBYaIHJy1sG0brF/vhMfuUKl8VLpC2dn1aBb37MfyrPasSktnXlEOn21+g12NvwUgcccJnNbqIu4573y6tm3qVq/kECgwROTwWOvMCVm0yPnG1syZzq/BfdBJTYWsLL5qfiQPJEXxVVo2JWmLIeAlqiCLhIpM0nyZtEjIJCs1k07NM+l1RFtO6NqW+Ngod/sme1FgiEj4BQKwZIlzg/3HH53hrQ0bnCMvjzebtueJLi1ZnV7GjuRdFCXnEEjcDKbS/2cCXnyFmST529Esuh1ZKe3oldmZSwf05chWv1mQWmqAAkNEatauXc7qvLuHt1auhJUrKVqxmp+25DM7thELGieyNDWK9c1ge8oOihI3YOP3LEIdnd+RNp5+9G3Vj3OP78cZx3bSir01QIEhIrXH7uGtn37asyf6rFlQVMTa2CSmt+/O++0zmJOew9a0+di4YIiUJJNS3IN28T05tnUPTu/Rk9N6dSQ6yutuf+oZBYaI1G5+P8yfvyc85syBxYsJBAJ8ktaG19u149u2UaxL20xh6lKIKnF+rjyORoXdiLWNiTJxRHviiNl9eGOJ8cUS440hxhdD7O4jKoYmiSl0bNGSbpktOCorQ/dRKlFgiEjdU1wMCxY44fHTT06gLF5Mya5dfJCexUcZzfihZQxrMwoojSqlwldGha+UgK+UgK8E6ysBXyl4Kg7+OdbgKWpCTEkGjSqaEGeSaORNpFFUEsmxKSTHp5DUKJnYqBhioqKJ8UU5R1QUcdHRNEtOJrNJKm2aNaZlelKdHzZTYIhI/WAtbN7s3GxfvNg5VqyAsjLnJvzuw1pn7S1rKauw5OMhz3rZhYcCa1gfMCwNBFgRBRsSPWxJrCA3sYSCRoX4Y0pxIEYzAAAHZklEQVTwxxQSiCmA6KLq1RfwYEpS8JUm4/XH4q2IwhuIwvfr4cOLD4/xYowX4/FgPD48Hi8+TzSNfAkkRSeTHJtESkIKqUkppKek0SQlhaaNk2mRmkyLtCSapjSKWDCFGhg1ueOeiEj1GQPNmzvHwIEh/Ug0kB48fiMQcLbN3bEDcnMhLw8KC6GoCAoLKcnbxaadhWzOL6KopIzisnJKysopKi+npLycYn8FWyv8bLF+tplycrzl5PnKyI8uodRXjt/rp8JTQZm3iOKocgJePwGvH7BYEwAT/BWw3nICMYUQXejUVhI8tu6nbmugNJGmm7szJGUgd14/glZH1OxMe11hiIjUlLKyX/d4//UoKnJCKiefjbm72JxfwJaCIraXl7PdX0ZuoJSdgTLyKWU7u1jWZDYVyRvBH0PG+uMZkjqAO/9yFS3atjjksjQkJSJSD/nL/Ux++hUm/vA285rMJJC0Ccpj6bXubLKnvnpI76khKRGResgX5WPEdZcwgkvwl/uZNOEFns5+hzZxzSL+2brCEBFp4LSnt4iIhJUCQ0REQqLAEBGRkCgwREQkJAoMEREJiQJDRERCosAQEZGQKDBERCQk9WrinjFmG7C2itPSge01UE5t01D7DQ237+p3w3I4/W5jrW1S1Un1KjBCYYzJDmVGY33TUPsNDbfv6nfDUhP91pCUiIiERIEhIiIhaYiBMcntAlzSUPsNDbfv6nfDEvF+N7h7GCIicmga4hWGiIgcgga1gZIxZihwIeAHvrfWPuRySRFjjPEC9wG9rbWnBdsGATcChcAGa+0oF0uMCGPMM0AASAXetta+0ED6/STO3+dEYJm19t6G0G8AY4wPmArsstZe1RD6bYyZA8wKPi0H/mKttRHvu7W2QRw4f5E+ZM8w3H+BDm7XFcH+Dgb6Ap8GnxvgMyAm+HwscKrbdUaw/x7gm4bW72AfpwAdG0q/cf5h9Dvg2Yby+7377/U+bRHve0MakuoHfGKD/yWBt4EB7pUTWdbaadba7yo1dQAWWWtLg8+nAQNrvrIaEw3k0MD6bYxJxpnAlUID6Hdw1OBHYFmwqaH8fnuMMfcZYyYbY84KtkW87w1pSCoNyK30PBc40qVa3LC//qe5VEtNGAM8RAPptzGmPc6/tI8Drge81PN+G2N6AhnW2heNMW2DzQ3i99taezL8Ohz3mjFmCTXQ94YUGDlAt0rPU4NtDUUOTp93q7f9N8bcCMyx1s40xnSkAfTbWrsCGBr8H8jLwL+p//2+EEgxxkzEGXLuCSyg/vf7V9ZavzHmM6ALsIQI970hDUnNAgYZY0zw+TnAVy7WU9NWAN2MMTHB54OBL12sJyKMMdcA+dbal4NNDaLfu1lr/ThXF2uo5/221t5qrb3KWns1cCcwEyco63W/96MvMI8a+LPeYK4wrLU7jTFTgdeNMX4g21q7xO26akAZgLW2whgzBnjFGFMIbAI+drWyMDPG9ANuBz42xvQNNt+BMzxVn/vdExgFFACNgDettWvr++/3PvyAvyH8OQcwxkwBioEEYJq1dk2wPaJ918Q9EREJSUMakhIRkcOgwBARkZAoMEREJCQKDBERCYkCQ6QKxpjXQzjnbGPMpWF6r0Gh1hY8/9JKs31FIqbBfK1WpCrGmGycr6budq+19gsgqtI5yTjrkDXG+VrjNdbalTj/+PJWOu+vOHN9/DjLVvzVWltR+b0OUEMm8KIxpr+1duk+r7UEngHigp81xVr7n+Bj72/eTCTMdIUhsscGa+2ASscX+znnLmC8tfZE4F5g/L4nGGPaAT2stSdba38HzAWGVvXhxpgewATgJODvxpjT9zllFPCAtXZg8JzzjTGJoXdP5PAoMEQOwBjjCS610d4Yc5MxphnQ2Vr7CYC19lugpTHmC5yVQSur/HcrGmcl0YN91hRgGHBpcELpRUAXY8ybxpik4GkfAqcbY7oBpwO/WGt3HV4vRUKnISmRPZobY74EKnD21CgG7gZWWGsfAai0tMxuK621Q4wxg3FWiMVau9IY86Mx5qvg+ywBrjvYB1trh+3zvAx4NHjsbvvEGLMIZ4HBrcCVxpiDDnGJhJMCQyTIWnu8McbYfZY/2CcjFhljfmet/dgYcwJ7ltXe973Gs5/hqv0Jbn7UCygFLNAVWBR8DM59kFtxVqONAuKBGJyNc3KAT4CdoXyWyOFQYIjsbQJwzT5tP1V6PBaYaoy5B2etnisO9EbGGA/OWj/pQFugZH/nWWtHBt9vmrV2njHmDeBPwYUEd79XNHAtTkj8FXjFWjs3+Nrl1emgyKFSYIjsrfm+DdbaMZUe5+F8++mAjDGTcfZaqcDZk2A9zrDUFwf7Mfbc5yjAGcqqXEMZsC74/vsNHpFIU2CI7C0+eBO7MovzL/4tB/m5iuCBtfbPBzrJGFN2gJc2AP8xxuy+if15cCgsx1p77j7nBtg7UH79bJFI0mq1IiISEn2tVkREQqLAEBGRkCgwREQkJAoMEREJiQJDRERCosAQEZGQKDBERCQk/x87uGDIWfaThwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = range(1, 51)\n",
    "y = [pair[1] for pair in freqTagged.most_common(50)]   # tuple(단어, 품사)\n",
    "yy = [pair[1] for pair in freqWord.most_common(50)]   # 단어\n",
    "yyy = [pair[1] for pair in freqTokens.most_common(50)]   # 단어\n",
    "\n",
    "plt.plot(x, y, \"r-\")\n",
    "plt.plot(x, yy, \"b-\")\n",
    "plt.plot(x, yyy, \"g-\")\n",
    "\n",
    "plt.xlabel(\"단어의 순위\")\n",
    "plt.ylabel(\"빈도\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '빈도')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "xx = range(1, 11)\n",
    "yyyy = [pair[1] for pair in freqPos.most_common(10)]   # 품사\n",
    "\n",
    "plt.plot(xx, yyyy, \"r-\")\n",
    "plt.xlabel(\"품사의 순위\")\n",
    "plt.ylabel(\"빈도\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 한글 POS Tagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "from konlpy.tag import Hannanum   # 한나눔"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = Hannanum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['내', '우리', '그', '이', '나', '는', '친구', '는', '잠', '을', '많', '이', '자', 'ㄴ다', '.']"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence = \"내 우리 그 이 나는 친구는 잠을 많이 잔다.\"\n",
    "obj.morphs(sentence)   # 문법적으로 의미 있는 최소한의 단위로 나눔 (띄어쓰기 단위 보다 많아짐. 영어에서와 다름)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('내', 'N'),\n",
       " ('우리', 'N'),\n",
       " ('그', 'M'),\n",
       " ('이', 'M'),\n",
       " ('나', 'N'),\n",
       " ('는', 'J'),\n",
       " ('친구', 'N'),\n",
       " ('는', 'J'),\n",
       " ('잠', 'N'),\n",
       " ('을', 'J'),\n",
       " ('많', 'P'),\n",
       " ('이', 'X'),\n",
       " ('자', 'P'),\n",
       " ('ㄴ다', 'E'),\n",
       " ('.', 'S')]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.pos(sentence)\n",
    "# obj.tagset   # 전체 tag set\n",
    "# obj.tagset[\"P\"]    # P(용언) : 동사"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['내', '우리', '나', '친구', '잠']"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.nouns(sentence)   # 명사"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from konlpy.tag import Kkma   # 꼬꼬마 (서울대)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('내', 'NP'),\n",
       " ('우리', 'NP'),\n",
       " ('그', 'MDT'),\n",
       " ('이', 'NNG'),\n",
       " ('나', 'VV'),\n",
       " ('는', 'ETD'),\n",
       " ('친구', 'NNG'),\n",
       " ('는', 'JX'),\n",
       " ('잠', 'NNG'),\n",
       " ('을', 'JKO'),\n",
       " ('많이', 'MAG'),\n",
       " ('자', 'VV'),\n",
       " ('ㄴ다', 'EFN'),\n",
       " ('.', 'SF')]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence = \"내 우리 그 이 나는 친구는 잠을 많이 잔다.\"\n",
    "\n",
    "obj = Kkma()\n",
    "obj.morphs(sentence)\n",
    "obj.pos(sentence) \n",
    "#obj.nouns(sentence) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- P(용언, 동사)를 Kkma가 더 정확히 분석해줌. Hannanum은 용언이 2개로 잘못 분석되어 있음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "from konlpy.tag import Komoran   # 코모란"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('내', 'NP'),\n",
       " ('우리', 'NP'),\n",
       " ('그', 'MM'),\n",
       " ('이', 'MM'),\n",
       " ('나', 'NP'),\n",
       " ('는', 'JX'),\n",
       " ('친구', 'NNG'),\n",
       " ('는', 'JX'),\n",
       " ('잠', 'NNG'),\n",
       " ('을', 'JKO'),\n",
       " ('많이', 'MAG'),\n",
       " ('자', 'VV'),\n",
       " ('ㄴ다', 'EF'),\n",
       " ('.', 'SF')]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence = \"내 우리 그 이 나는 친구는 잠을 많이 잔다.\"\n",
    "\n",
    "obj = Komoran()\n",
    "obj.morphs(sentence)\n",
    "obj.pos(sentence) \n",
    "#obj.nouns(sentence) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "from konlpy.tag import Okt   # twitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('내', 'Noun'),\n",
       " ('우리', 'Noun'),\n",
       " ('그', 'Noun'),\n",
       " ('이', 'Noun'),\n",
       " ('나', 'Noun'),\n",
       " ('는', 'Josa'),\n",
       " ('친구', 'Noun'),\n",
       " ('는', 'Josa'),\n",
       " ('잠', 'Noun'),\n",
       " ('을', 'Josa'),\n",
       " ('많이', 'Adverb'),\n",
       " ('잔다', 'Verb'),\n",
       " ('.', 'Punctuation')]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence = \"내 우리 그 이 나는 친구는 잠을 많이 잔다.\"\n",
    "\n",
    "obj = Okt()\n",
    "obj.morphs(sentence)\n",
    "obj.pos(sentence) \n",
    "#obj.nouns(sentence) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 한글 형태소 분석기 성능 비교"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 형태소 분리 성능 : Kkma = Komoran = Okt > Hannanum\n",
    "- 품사 분석 성능 : Kkma = Komoran = Okt > Hannanum\n",
    "- 명사 분석 성능 : \n",
    "    - Kkma = Komoran = Okt => 대명사 포함(모든 명사)\n",
    "    - Hannanum => 명사에 대명사 제외"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 국립국어원 세종21 : 말뭉치 검색 기능"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- https://ithub.korean.go.kr/user/corpus/corpusSearchManager.do\n",
    "\n",
    "- 특정 단어를 검색해서(예: \"친구\"), 검색 결과를 하나를 선택(예:고향친구) 하면, 문맥정보와 형테/형태의미 정보를 제공해줌<br>\n",
    "\n",
    "  [문맥정보]<br>\n",
    "   이전 문장 :\t고향 벌교로 내려가 있던 그때 남편이 나타났다.<br>\n",
    "   현재 문장 :\t구해지지 않는 직장을 구하느니 고향친구 경자가 시집가느라 내놓은 인조꽃가게를 돈벌면 갚기로 하고 우선 넘겨받았다.<br>\n",
    "   다음 문장 :\t경자는 고등학교 다닐 때부터 저 혼자 사모하던 담임선생이 상처를 하자 옳다구나, 딴생각할 것도 없이 바로 그 자리에 들어앉았다.<br>\n",
    "   [형태/형태의미 정보]<br>\n",
    "   \n",
    "| 형태 | 형태의미 |\n",
    "|:------:|:----------:|\n",
    "|구해지지|\t구하/VV+아/EC+지/VX+지/EC|\n",
    "|않는|\t않/VX+는/ETM|\n",
    "|직장을|\t직장/NNG+을/JKO|\n",
    "|구하느니|\t구하/VV+느니/EC|\n",
    "|고향친구|\t고향/NNG+친구/NNG|\n",
    "|경자가|\t경자/NNP+가/JKS|\n",
    "|시집가느라|\t시집가/VV+느라/EC|\n",
    "|내놓은|\t내놓/VV+은/ETM|\n",
    "|인조꽃가게를|\t인조/NNP+꽃/NNG+가게/NNG+를/JKO|\n",
    "|돈벌면|\t돈/NNG+벌/VV+면/EC|\n",
    "|갚기로|\t갚/VV+기/ETN+로/JKB|\n",
    "|하고|\t하/VV+고/EC|\n",
    "|우선|\t우선/MAG|\n",
    "|넘겨받았다.|\t넘겨받/VV+았/EP+다/EF+./SF|\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 한글 실제 데이터(법전) pos tagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 헌법 법전\n",
    "from konlpy.corpus import kolaw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus = kolaw.open(kolaw.fileids()[0]).read()\n",
    "tokens = Kkma().pos(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4640, 10053, ('대한민국', 'NNG'))"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(word_tokenize(corpus)), len(tokens), tokens[0]   # POS tagging 후 말뭉치가 더 커졌음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "rawList = FreqDist()\n",
    "pairList = FreqDist()\n",
    "wordList = FreqDist()\n",
    "posList = FreqDist()\n",
    "\n",
    "for term in word_tokenize(corpus):\n",
    "    rawList[term] += 1\n",
    "    \n",
    "for pair in tokens:\n",
    "    pairList[pair] += 1\n",
    "    wordList[pair[0]] += 1\n",
    "    posList[pair[1]] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2023, 1294, 1247, 38)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(rawList), len(pairList), len(wordList), len(posList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '빈도')"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "x = range(1, 51)\n",
    "y = [pair[1] for pair in rawList.most_common(50)]  \n",
    "yy = [pair[1] for pair in pairList.most_common(50)]   \n",
    "yyy = [pair[1] for pair in wordList.most_common(50)]   \n",
    "yyyy = [pair[1] for pair in posList.most_common(50)]   \n",
    "\n",
    "plt.plot(x, y, \"r-\")\n",
    "plt.plot(x, yy, \"b-\")\n",
    "plt.plot(x, yyy, \"g-\")\n",
    "#plt.plot(x, yyy, \"k--\")\n",
    "\n",
    "plt.xlabel(\"단어의 순위\")\n",
    "plt.ylabel(\"빈도\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '빈도')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# log scale로 그리면 더 선형적으로 보임 (blue, green이 red 보다 더 일반화 되어 있음을 알 수 있음)\n",
    "import math\n",
    "\n",
    "x = range(1, 51)\n",
    "_y = [math.log10(_) for _ in y]\n",
    "_yy = [math.log10(_) for _ in yy]\n",
    "_yyy = [math.log10(_) for _ in yyy]\n",
    "\n",
    "plt.plot(x, _y, \"r-\")\n",
    "plt.plot(x, _yy, \"b-\")\n",
    "plt.plot(x, _yyy, \"g-\")\n",
    "\n",
    "plt.xlabel(\"단어의 순위\")\n",
    "plt.ylabel(\"빈도\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### naver 뉴스 기사 POS tagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '빈도')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "import os\n",
    "\n",
    "# 뉴스 기사 읽어오기\n",
    "path = \"naver_news/2019-03-20_02-07\"\n",
    "corpus = \"\"\n",
    "for file in [_ for _ in os.listdir(path) if _.endswith(\".txt\")]:\n",
    "    with open(\"{0}/{1}\".format(path, file), encoding=\"utf8\") as fp:\n",
    "        corpus += fp.read()\n",
    "\n",
    "# word tokenize\n",
    "rawList = FreqDist()\n",
    "for term in word_tokenize(corpus):\n",
    "    rawList[term] += 1\n",
    "\n",
    "# Kkma POS tagging\n",
    "tokens = Kkma().pos(corpus)\n",
    "\n",
    "pairList = FreqDist()\n",
    "wordList = FreqDist()\n",
    "posList = FreqDist()\n",
    "\n",
    "for pair in tokens:\n",
    "    pairList[pair] += 1\n",
    "    wordList[pair[0]] += 1\n",
    "    posList[pair[1]] += 1\n",
    "    \n",
    "# 그래프 그리기\n",
    "x = range(1, 51)\n",
    "y = [pair[1] for pair in rawList.most_common(50)]  \n",
    "yy = [pair[1] for pair in pairList.most_common(50)]   \n",
    "yyy = [pair[1] for pair in wordList.most_common(50)]   \n",
    "#yyyy = [pair[1] for pair in posList.most_common(50)]   \n",
    "\n",
    "plt.plot(x, y, \"r-\")\n",
    "plt.plot(x, yy, \"b-\")\n",
    "plt.plot(x, yyy, \"g-\")\n",
    "#plt.plot(x, yyy, \"k--\")\n",
    "\n",
    "plt.xlabel(\"단어의 순위\")\n",
    "plt.ylabel(\"빈도\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '빈도')"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# 음절 단위 Ngram 사용해보기\n",
    "ngramList = FreqDist()\n",
    "\n",
    "for term in word_tokenize(corpus):\n",
    "    for token in ngramUmjeol(term):\n",
    "        ngramList[term] += 1\n",
    "        \n",
    "# 그래프 그리기\n",
    "ngram_y = [pair[1] for pair in ngramList.most_common(50)]  \n",
    "ngram_yy = [math.log10(_) for _ in ngram_y]\n",
    "\n",
    "plt.plot(x, y, \"r-\")\n",
    "plt.plot(x, yy, \"b-\")\n",
    "plt.plot(x, yyy, \"g-\")\n",
    "plt.plot(x, ngram_y, \"y-\")\n",
    "\n",
    "plt.xlabel(\"단어의 순위\")\n",
    "plt.ylabel(\"빈도\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '빈도')"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(x, _y, \"r-\")\n",
    "plt.plot(x, _yy, \"b-\")\n",
    "plt.plot(x, _yyy, \"g-\")\n",
    "plt.plot(x, ngram_yy, \"y-\")\n",
    "\n",
    "plt.xlabel(\"단어의 순위\")\n",
    "plt.ylabel(\"빈도\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 한글 POS taggers 비교"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Mecab이 성능이 가장 좋지만, windows 지원 안함\n",
    "- POS Taggers 비교\n",
    "\n",
    "![pos_taggers](./images/POS_tagger_비교.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collocations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- A sequence of words or terms\n",
    "- co-occur more often than would be expected by chance\n",
    "- frequency counting, Pointwise Mutual Information(PMI), hypothesis testing(t-test and chi-square), ...\n",
    "\n",
    "![collocations](./images/collocations.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.collocations import BigramAssocMeasures, BigramCollocationFinder   # 어절 단위 2개의 쌍을 찾음\n",
    "from nltk.corpus import gutenberg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import word_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus = gutenberg.open(gutenberg.fileids()[0]).read()\n",
    "tokens = word_tokenize(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('26th', 'ult.'),\n",
       " ('Abominable', 'scoundrel'),\n",
       " ('Agricultural', 'Reports'),\n",
       " ('Austen', '1816'),\n",
       " ('Baronne', \"d'Almane\"),\n",
       " ('Candles', 'everywhere.'),\n",
       " ('Clayton', 'Park'),\n",
       " ('Comtesse', \"d'Ostalis\"),\n",
       " ('DEAR', 'MADAM'),\n",
       " ('Farmer', 'Mitchell')]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigram = BigramCollocationFinder.from_words(tokens)\n",
    "bigram.nbest(BigramAssocMeasures().pmi, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('&', 'c.'),\n",
       " ('10,000', 'l.'),\n",
       " ('26th', 'ult.'),\n",
       " ('Abominable', 'scoundrel'),\n",
       " ('Agricultural', 'Reports'),\n",
       " ('Austen', '1816'),\n",
       " ('Baronne', \"d'Almane\"),\n",
       " ('Candles', 'everywhere.'),\n",
       " ('Clayton', 'Park'),\n",
       " ('Comtesse', \"d'Ostalis\")]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigram.nbest(BigramAssocMeasures().chi_sq, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('26th', 'ult.'), ('Abominable', 'scoundrel'), ('Agricultural', 'Reports'), ('Austen', '1816'), ('Baronne', \"d'Almane\"), ('Candles', 'everywhere.'), ('Clayton', 'Park'), ('Comtesse', \"d'Ostalis\"), ('DEAR', 'MADAM'), ('Farmer', 'Mitchell')]\n",
      "[('&', 'c.'), ('10,000', 'l.'), ('26th', 'ult.'), ('Abominable', 'scoundrel'), ('Agricultural', 'Reports'), ('Austen', '1816'), ('Baronne', \"d'Almane\"), ('Candles', 'everywhere.'), ('Clayton', 'Park'), ('Comtesse', \"d'Ostalis\")]\n"
     ]
    }
   ],
   "source": [
    "# 형태소 분석기로 token 만들기\n",
    "from nltk import pos_tag\n",
    "\n",
    "corpus = gutenberg.open(gutenberg.fileids()[0]).read()\n",
    "#tokens = [row for row in pos_tag(word_tokenize(corpus))]\n",
    "tokens = [row[0] for row in pos_tag(word_tokenize(corpus))]\n",
    "\n",
    "bigram = BigramCollocationFinder.from_words(tokens)\n",
    "print(bigram.nbest(BigramAssocMeasures().pmi, 10))\n",
    "print(bigram.nbest(BigramAssocMeasures().chi_sq, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(\"'K팝\", '경정에게'), (\"'MWC\", \"2019'에서\"), (\"'No\", '서울경제를'), (\"'fn파스'▶\", '속보이는'), (\"'그것\", '공통된'), (\"'길고\", '물체에'), (\"'김정은\", '수석대변인'), (\"'김학의ㆍ장자연\", '진상조사'), (\"'깜짝\", '출시됐다가'), (\"'깡통전세\", '문제의')]\n",
      "[(\"'K팝\", '경정에게'), (\"'MWC\", \"2019'에서\"), (\"'No\", '서울경제를'), (\"'fn파스'▶\", '속보이는'), (\"'그것\", '공통된'), (\"'길고\", '물체에'), (\"'김정은\", '수석대변인'), (\"'김학의ㆍ장자연\", '진상조사'), (\"'깜짝\", '출시됐다가'), (\"'깡통전세\", '문제의')]\n"
     ]
    }
   ],
   "source": [
    "# 형태소 분석기로 token 만들기\n",
    "result = pos_tag(word_tokenize(corpus))\n",
    "tokens = [row[0] for row in result if len(row[0]) > 2]   # 짧은 글자 처리\n",
    "\n",
    "bigram = BigramCollocationFinder.from_words(tokens)\n",
    "print(bigram.nbest(BigramAssocMeasures().pmi, 10))\n",
    "print(bigram.nbest(BigramAssocMeasures().chi_sq, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('가부', '동수'), ('강제', '노역'), ('경자', '유전'), ('공무', '담임'), ('공중', '도덕'), ('과반', '수가'), ('교전', '상태'), ('기본적', '인권'), ('노인과', '청소년'), ('다수', '표')]\n",
      "[('가부', '동수'), ('강제', '노역'), ('경자', '유전'), ('공무', '담임'), ('공중', '도덕'), ('과반', '수가'), ('교전', '상태'), ('기본적', '인권'), ('내부', '규율'), ('노인과', '청소년')]\n"
     ]
    }
   ],
   "source": [
    "# 한글에 적용\n",
    "corpus = kolaw.open(kolaw.fileids()[0]).read()\n",
    "\n",
    "tokens = [pair[0] for pair in Kkma().pos(corpus)]\n",
    "\n",
    "bigram = BigramCollocationFinder.from_words(tokens)\n",
    "print(bigram.nbest(BigramAssocMeasures().pmi, 10))\n",
    "print(bigram.nbest(BigramAssocMeasures().chi_sq, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(\"'K팝\", '공연'), (\"'MWC\", \"2019'에서\"), (\"'fn파스'▶\", '속보이는'), (\"'갤럭시\", '버즈'), (\"'그것\", '공통된'), (\"'김정은\", '수석대변인'), (\"'깜짝\", '출시됐다가'), (\"'깡통전세\", '문제의'), (\"'대왕젤리'라는\", '애칭이'), (\"'도넛\", '탱크')]\n",
      "[(\"'K팝\", '공연'), (\"'MWC\", \"2019'에서\"), (\"'fn파스'▶\", '속보이는'), (\"'갤럭시\", '버즈'), (\"'그것\", '공통된'), (\"'김정은\", '수석대변인'), (\"'깜짝\", '출시됐다가'), (\"'깡통전세\", '문제의'), (\"'뉴스APP\", '이슈부터'), (\"'대만\", '대왕젤리')]\n"
     ]
    }
   ],
   "source": [
    "# 뉴스 기사 읽어오기\n",
    "path = \"naver_news/2019-03-20_02-07\"\n",
    "corpus = \"\"\n",
    "for file in [_ for _ in os.listdir(path) if _.endswith(\".txt\")]:\n",
    "    with open(\"{0}/{1}\".format(path, file), encoding=\"utf8\") as fp:\n",
    "        corpus += fp.read()\n",
    "\n",
    "# tokens = [pair[0] for pair in Kkma().pos(corpus)]\n",
    "result = pos_tag(word_tokenize(corpus))\n",
    "tokens = [row[0] for row in result if len(row[0]) > 1]   # 짧은 글자 처리\n",
    "\n",
    "bigram = BigramCollocationFinder.from_words(tokens)\n",
    "print(bigram.nbest(BigramAssocMeasures().pmi, 10))\n",
    "print(bigram.nbest(BigramAssocMeasures().chi_sq, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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