3. Named Entity Recognition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "    <div style=\"width: 150px; float: left;\"> <img src=\"data/D4Sci_logo_ball.png\" alt=\"Data For Science, Inc\" align=\"left\" border=\"0\" width=160px> </div>\n",
    "    <div style=\"float: left; margin-left: 10px;\"> <h1>Natural Language Processing</h1>\n",
    "<h1>Named Entity Recognition</h1>\n",
    "        <p>Bruno Gonçalves<br/>\n",
    "        <a href=\"http://www.data4sci.com/\">www.data4sci.com</a><br/>\n",
    "            @bgoncalves, @data4sci</p></div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display\n",
    "\n",
    "from collections import Counter\n",
    "from pprint import pprint\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "import tqdm as tq\n",
    "from tqdm import tqdm\n",
    "\n",
    "import string\n",
    "\n",
    "import nltk\n",
    "from nltk.corpus import treebank\n",
    "from nltk.tag import hmm\n",
    "\n",
    "import watermark\n",
    "\n",
    "%load_ext watermark\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by print out the versions of the libraries we're using for future reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.8.5\n",
      "IPython version      : 7.19.0\n",
      "\n",
      "Compiler    : Clang 10.0.0 \n",
      "OS          : Darwin\n",
      "Release     : 20.5.0\n",
      "Machine     : x86_64\n",
      "Processor   : i386\n",
      "CPU cores   : 16\n",
      "Architecture: 64bit\n",
      "\n",
      "Git hash: ada0d41c33546e250d7a5ace74624d6ece6f91ef\n",
      "\n",
      "nltk      : 3.5\n",
      "watermark : 2.1.0\n",
      "tqdm      : 4.62.0\n",
      "pandas    : 1.1.3\n",
      "numpy     : 1.19.2\n",
      "json      : 2.0.9\n",
      "matplotlib: 3.3.2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark -n -v -m -g -iv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load default figure style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('./d4sci.mplstyle')\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part-Of-Speech Tagging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's consider a simple example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"They refuse to permit us to obtain the refuse permit\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to tokenize the text. Since we'll be feeding this text back to nltk for the pos analysis it's best ot use the default tokenizer as that preserves punctuation characters that provide important clues about the meaning and grammatical role of each word."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokens = nltk.word_tokenize(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally passing the tokens to the pos tagger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tags = nltk.pos_tag(tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and, as expected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('They', 'PRP'),\n",
      " ('refuse', 'VBP'),\n",
      " ('to', 'TO'),\n",
      " ('permit', 'VB'),\n",
      " ('us', 'PRP'),\n",
      " ('to', 'TO'),\n",
      " ('obtain', 'VB'),\n",
      " ('the', 'DT'),\n",
      " ('refuse', 'NN'),\n",
      " ('permit', 'NN')]\n"
     ]
    }
   ],
   "source": [
    "pprint(tags)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily obtain the full list of tags for the default tagger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "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": "markdown",
   "metadata": {},
   "source": [
    "Training a pos_tagger is also straightforward. We'll use the pre-tagged TreeBank corpus as our training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = treebank.tagged_sents()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this example, we'll use the Hidden Markov Model Tagger. We start by instanciating the HMM trainer object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer = hmm.HiddenMarkovModelTrainer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then training it with the upenn training corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "tagger = trainer.train_supervised(train_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so we can now use our trained tokenizer just like we used pos_tag()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('They', 'PRP'),\n",
      " ('refuse', 'VBP'),\n",
      " ('to', 'TO'),\n",
      " ('permit', 'VB'),\n",
      " ('us', 'PRP'),\n",
      " ('to', 'TO'),\n",
      " ('obtain', 'VB'),\n",
      " ('the', 'DT'),\n",
      " ('refuse', 'VBP'),\n",
      " ('permit', 'VB')]\n"
     ]
    }
   ],
   "source": [
    "pprint(tagger.tag(nltk.word_tokenize(text)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll note that in this simple example, the HMM tagger mistags the second instances of refuse and permit, highlighting the fact that these algorithms are statistic in nature and you should always check the results to make sure they make sense for your specific application."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we use an even simpler tagger, the results are even less appealing. Let us defined a set of regular expressions to map tokens to POS categories (based on https://tedboy.github.io/nlps/generated/generated/nltk.RegexpTagger.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "regexp = [\n",
    "    (r'^-?[0-9]+(.[0-9]+)?$', 'CD'),   # cardinal numbers\n",
    "    (r'(The|the|A|a|An|an)$', 'AT'),   # articles\n",
    "    (r'.*able$', 'JJ'),                # adjectives\n",
    "    (r'.*ness$', 'NN'),                # nouns formed from adjectives\n",
    "    (r'.*ly$', 'RB'),                  # adverbs\n",
    "    (r'.*s$', 'NNS'),                  # plural nouns\n",
    "    (r'.*ing$', 'VBG'),                # gerunds\n",
    "    (r'.*ed$', 'VBD'),                 # past tense verbs\n",
    "    (r'.*', 'NN')                      # nouns (default)\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now er create a RegexpTagger based on these definitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "regexp_tagger = nltk.tag.RegexpTagger(regexp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and use it to tag our text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('They', 'NN'),\n",
       " ('refuse', 'NN'),\n",
       " ('to', 'NN'),\n",
       " ('permit', 'NN'),\n",
       " ('us', 'NNS'),\n",
       " ('to', 'NN'),\n",
       " ('obtain', 'NN'),\n",
       " ('the', 'AT'),\n",
       " ('refuse', 'NN'),\n",
       " ('permit', 'NN')]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regexp_tagger.tag(nltk.word_tokenize(text))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results are... less than impressive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chunking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us go back to our favourite nursery rhyme:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"\"\"Mary had a little lamb, little lamb,\n",
    "    little lamb. Mary had a little lamb\n",
    "    whose fleece was white as snow.\n",
    "    And everywhere that Mary went\n",
    "    Mary went, MARY went. Everywhere\n",
    "    that mary went,\n",
    "    The lamb was sure to go\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going to extract a single sentence and extract the POS tags for each token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentences = nltk.sent_tokenize(text)\n",
    "text = sentences[1]\n",
    "tokens = nltk.word_tokenize(text)\n",
    "pos_tags = nltk.pos_tag(tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far so good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Mary', 'NNP'),\n",
       " ('had', 'VBD'),\n",
       " ('a', 'DT'),\n",
       " ('little', 'JJ'),\n",
       " ('lamb', 'NN'),\n",
       " ('whose', 'WP$'),\n",
       " ('fleece', 'NN'),\n",
       " ('was', 'VBD'),\n",
       " ('white', 'JJ'),\n",
       " ('as', 'IN'),\n",
       " ('snow', 'NN'),\n",
       " ('.', '.')]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_tags"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we can proceed to chunking this sentence, we need to create a grammar defining our how chunks should look like. For simplicity, we'll just define a rule for Noun Phrases to be an optional Determiners followed by 0 or more adjectives and one or more nouns. (from https://www.nltk.org/book/ch07.html#noun-phrase-chunking)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "grammar = \"NP: {<DT>?<JJ.*>*<NN.*>+}\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use a RegexpParser as a chunk parser after initializing it with this simple grammar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp = nltk.RegexpParser(grammar)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and we can now chunk the tags of our sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = cp.parse(pos_tags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Tree('S', [Tree('NP', [('Mary', 'NNP')]), ('had', 'VBD'), Tree('NP', [('a', 'DT'), ('little', 'JJ'), ('lamb', 'NN')]), ('whose', 'WP$'), Tree('NP', [('fleece', 'NN')]), ('was', 'VBD'), ('white', 'JJ'), ('as', 'IN'), Tree('NP', [('snow', 'NN')]), ('.', '.')])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the algorithm correctly identified \"Mary\", \"a little lamb\", \"fleece\" and \"snow\" as Noun Phrases that follow our grammars definition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now all we have to do is traverse the tree looking for nodes labeled 'NP' and print the leaves they contain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Mary\n",
      "1 a little lamb\n",
      "2 fleece\n",
      "3 snow\n"
     ]
    }
   ],
   "source": [
    "for i, subtree in enumerate(result.subtrees(filter=lambda t: t.label() == 'NP')):\n",
    "    print(i, \" \".join([token for token, pos in subtree.leaves()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The procedure is the same, regardless of how complex is the grammar we define"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "grammar = r\"\"\"\n",
    "  NP: {<DT>?<JJ.*>*<NN.*>+}    # Chunk sequences of DT, JJ, NN\n",
    "  PP: {<IN><NP>}               # Chunk prepositions followed by NP\n",
    "  VP: {<VB.*><NP|PP|CLAUSE>+$} # Chunk verbs and their arguments\n",
    "  CLAUSE: {<NP><VP>}           # Chunk NP, VP\n",
    "  \"\"\"\n",
    "cp = nltk.RegexpParser(grammar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = cp.parse(pos_tags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Tree('S', [Tree('NP', [('Mary', 'NNP')]), ('had', 'VBD'), Tree('NP', [('a', 'DT'), ('little', 'JJ'), ('lamb', 'NN')]), ('whose', 'WP$'), Tree('NP', [('fleece', 'NN')]), ('was', 'VBD'), ('white', 'JJ'), Tree('PP', [('as', 'IN'), Tree('NP', [('snow', 'NN')])]), ('.', '.')])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But as long as the definition of the Noun Phrases remains the same, so does the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Mary\n",
      "1 a little lamb\n",
      "2 fleece\n",
      "3 snow\n"
     ]
    }
   ],
   "source": [
    "for i, subtree in enumerate(result.subtrees(filter=lambda t: t.label() == 'NP')):\n",
    "    print(i, \" \".join([token for token, pos in subtree.leaves()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can just as easily extract whatever node we are interested in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 as snow\n"
     ]
    }
   ],
   "source": [
    "for i, subtree in enumerate(result.subtrees(filter=lambda t: t.label() == 'PP')):\n",
    "    print(i, \" \".join([token for token, pos in subtree.leaves()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chinking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The chunk you remove from your chunk is your chink! This grammar starts by assigning everyhting into a single chunk and them breaking off past tense verbs (VDB) and propositions (IN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "grammar = r\"\"\"\n",
    "  NP:\n",
    "    {<.*>+}          # Chunk everything\n",
    "    }<VBD|IN>+{      # Chink sequences of VBD and IN\n",
    "  \"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So our example now looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Tree('S', [Tree('NP', [('Mary', 'NNP')]), ('had', 'VBD'), Tree('NP', [('a', 'DT'), ('little', 'JJ'), ('lamb', 'NN'), ('whose', 'WP$'), ('fleece', 'NN')]), ('was', 'VBD'), Tree('NP', [('white', 'JJ')]), ('as', 'IN'), Tree('NP', [('snow', 'NN'), ('.', '.')])])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp = nltk.RegexpParser(grammar)\n",
    "cp.parse(pos_tags)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Named Entity Recognition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"\"\"Two U.S. senators on Wednesday pressed the Federal Trade Commission to probe Tesla\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We tokenize and add POS tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokens = nltk.word_tokenize(sentence)\n",
    "pos_tags = nltk.pos_tag(tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And define our Chunking grammar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "grammar = r\"\"\"\n",
    "  NP: {<DT>?<JJ.*>*<NN.*>+}    # Chunk sequences of DT, JJ, NN\n",
    "  \"\"\"\n",
    "cp = nltk.RegexpParser(grammar)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform simple Chunking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "chunked_tags = cp.parse(pos_tags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Tree('S', [('Two', 'CD'), Tree('NP', [('U.S.', 'NNP'), ('senators', 'NNS')]), ('on', 'IN'), Tree('NP', [('Wednesday', 'NNP')]), ('pressed', 'VBD'), Tree('NP', [('the', 'DT'), ('Federal', 'NNP'), ('Trade', 'NNP'), ('Commission', 'NNP')]), ('to', 'TO'), ('probe', 'VB'), Tree('NP', [('Tesla', 'NNP')])])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chunked_tags"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And, finally, identify the Named Entities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Tree('S', [('Two', 'CD'), Tree('GPE', [('U.S.', 'NNP')]), ('senators', 'NNS'), ('on', 'IN'), ('Wednesday', 'NNP'), ('pressed', 'VBD'), ('the', 'DT'), Tree('ORGANIZATION', [('Federal', 'NNP'), ('Trade', 'NNP'), ('Commission', 'NNP')]), ('to', 'TO'), ('probe', 'VB'), Tree('PERSON', [('Tesla', 'NNP')])])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.ne_chunk(pos_tags)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "     <img src=\"data/D4Sci_logo_full.png\" alt=\"Data For Science, Inc\" align=\"center\" border=\"0\" width=300px> \n",
    "</div>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}