chunking.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     /Users/leonelgalan/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     /Users/leonelgalan/nltk_data...\n",
      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
      "[nltk_data]       date!\n",
      "[nltk_data] Downloading package tagsets to\n",
      "[nltk_data]     /Users/leonelgalan/nltk_data...\n",
      "[nltk_data]   Package tagsets is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "from IPython.display import display\n",
    "import nltk\n",
    "\n",
    "nltk.download('punkt')\n",
    "nltk.download('averaged_perceptron_tagger')\n",
    "nltk.download('tagsets')\n",
    "\n",
    "\n",
    "def print_phrases(sentence, grammar):\n",
    "    chunkParser = nltk.RegexpParser(grammar)\n",
    "    words = nltk.word_tokenize(sentence)\n",
    "    tagged = nltk.pos_tag(words)\n",
    "    chunked = chunkParser.parse(tagged)\n",
    "    phrases = [\n",
    "        ' '.join([x[0] for x in r.leaves()])\n",
    "        for r in chunked\n",
    "        if type(r) == nltk.tree.Tree and r.label() in ['NP']\n",
    "    ]\n",
    "    display(chunked)\n",
    "    print('{}\\n{}\\n{}\\n\\n'.format(sentence, chunked, phrases))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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": [
    "nltk.help.upenn_tagset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "Tree('S', [Tree('NP', [('Car', 'NN')])])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Car\n",
      "(S (NP Car/NN))\n",
      "['Car']\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Tree('S', [Tree('NP', [('The', 'DT'), ('car', 'NN')])])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The car\n",
      "(S (NP The/DT car/NN))\n",
      "['The car']\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Tree('S', [Tree('NP', [('The', 'DT'), ('house', 'NN')]), ('and', 'CC'), Tree('NP', [('tree', 'NN')])])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The house and tree\n",
      "(S (NP The/DT house/NN) and/CC (NP tree/NN))\n",
      "['The house', 'tree']\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Tree('S', [Tree('NP', [('The', 'DT'), ('house', 'NN')]), (',', ','), Tree('NP', [('the', 'DT'), ('tree', 'NN')]), (',', ','), ('and', 'CC'), Tree('NP', [('the', 'DT'), ('car', 'NN')])])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The house, the tree, and the car\n",
      "(S\n",
      "  (NP The/DT house/NN)\n",
      "  ,/,\n",
      "  (NP the/DT tree/NN)\n",
      "  ,/,\n",
      "  and/CC\n",
      "  (NP the/DT car/NN))\n",
      "['The house', 'the tree', 'the car']\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Tree('S', [Tree('NP', [('The', 'DT'), ('tree', 'NN')]), (',', ','), Tree('NP', [('house', 'NN')]), ('and', 'CC'), Tree('NP', [('car', 'NN')])])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The tree, house and car\n",
      "(S (NP The/DT tree/NN) ,/, (NP house/NN) and/CC (NP car/NN))\n",
      "['The tree', 'house', 'car']\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sentences = [\n",
    "    'Car',\n",
    "    'The car',\n",
    "    'The house and tree',\n",
    "    'The house, the tree, and the car',\n",
    "    'The tree, house and car'\n",
    "]\n",
    "\n",
    "# Original Grammar:\n",
    "# http://www.nltk.org/book/ch07.html#code-cascaded-chunker\n",
    "\n",
    "grammar = r\"\"\"\n",
    "  NP: {<DT|JJ|NN.*>+}\n",
    "\"\"\"\n",
    "\n",
    "foo = [print_phrases(sentence, grammar) for sentence in sentences]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pipenv",
   "language": "python",
   "name": "pipenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

requirements.txt

nltk