nltk_ch_02.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['austen-emma.txt',\n",
       " 'austen-persuasion.txt',\n",
       " 'austen-sense.txt',\n",
       " 'bible-kjv.txt',\n",
       " 'blake-poems.txt',\n",
       " 'bryant-stories.txt',\n",
       " 'burgess-busterbrown.txt',\n",
       " 'carroll-alice.txt',\n",
       " 'chesterton-ball.txt',\n",
       " 'chesterton-brown.txt',\n",
       " 'chesterton-thursday.txt',\n",
       " 'edgeworth-parents.txt',\n",
       " 'melville-moby_dick.txt',\n",
       " 'milton-paradise.txt',\n",
       " 'shakespeare-caesar.txt',\n",
       " 'shakespeare-hamlet.txt',\n",
       " 'shakespeare-macbeth.txt',\n",
       " 'whitman-leaves.txt']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.corpus.gutenberg.fileids()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "emma = nltk.corpus.gutenberg.words(\"austen-emma.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "192427"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(emma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['austen-emma.txt',\n",
       " 'austen-persuasion.txt',\n",
       " 'austen-sense.txt',\n",
       " 'bible-kjv.txt',\n",
       " 'blake-poems.txt',\n",
       " 'bryant-stories.txt',\n",
       " 'burgess-busterbrown.txt',\n",
       " 'carroll-alice.txt',\n",
       " 'chesterton-ball.txt',\n",
       " 'chesterton-brown.txt',\n",
       " 'chesterton-thursday.txt',\n",
       " 'edgeworth-parents.txt',\n",
       " 'melville-moby_dick.txt',\n",
       " 'milton-paradise.txt',\n",
       " 'shakespeare-caesar.txt',\n",
       " 'shakespeare-hamlet.txt',\n",
       " 'shakespeare-macbeth.txt',\n",
       " 'whitman-leaves.txt']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.corpus import gutenberg\n",
    "gutenberg.fileids()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg word len \t Avg sentence len \t occurance vocab \t file id\n",
      "5\t\t25\t\t26\t\tausten-emma.txt\n",
      "5\t\t26\t\t17\t\tausten-persuasion.txt\n",
      "5\t\t28\t\t22\t\tausten-sense.txt\n",
      "4\t\t34\t\t79\t\tbible-kjv.txt\n",
      "5\t\t19\t\t5\t\tblake-poems.txt\n",
      "4\t\t19\t\t14\t\tbryant-stories.txt\n",
      "4\t\t18\t\t12\t\tburgess-busterbrown.txt\n",
      "4\t\t20\t\t13\t\tcarroll-alice.txt\n",
      "5\t\t20\t\t12\t\tchesterton-ball.txt\n",
      "5\t\t23\t\t11\t\tchesterton-brown.txt\n",
      "5\t\t18\t\t11\t\tchesterton-thursday.txt\n",
      "4\t\t21\t\t25\t\tedgeworth-parents.txt\n",
      "5\t\t26\t\t15\t\tmelville-moby_dick.txt\n",
      "5\t\t52\t\t11\t\tmilton-paradise.txt\n",
      "4\t\t12\t\t9\t\tshakespeare-caesar.txt\n",
      "4\t\t12\t\t8\t\tshakespeare-hamlet.txt\n",
      "4\t\t12\t\t7\t\tshakespeare-macbeth.txt\n",
      "5\t\t36\t\t12\t\twhitman-leaves.txt\n"
     ]
    }
   ],
   "source": [
    "print(\"Avg word len \\t Avg sentence len \\t occurance vocab \\t file id\")\n",
    "for fileid in gutenberg.fileids():\n",
    "    num_chars = len(gutenberg.raw(fileid))\n",
    "    num_words = len(gutenberg.words(fileid))\n",
    "    num_sents = len(gutenberg.sents(fileid))\n",
    "    num_vocab = len(set(w.lower() for w in gutenberg.words(fileid)))\n",
    "    print(\"{}\\t\\t{}\\t\\t{}\\t\\t{}\".format(round(num_chars/num_words), round(num_words/num_sents), round(num_words/num_vocab), fileid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['adventure',\n",
       " 'belles_lettres',\n",
       " 'editorial',\n",
       " 'fiction',\n",
       " 'government',\n",
       " 'hobbies',\n",
       " 'humor',\n",
       " 'learned',\n",
       " 'lore',\n",
       " 'mystery',\n",
       " 'news',\n",
       " 'religion',\n",
       " 'reviews',\n",
       " 'romance',\n",
       " 'science_fiction']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.corpus import brown\n",
    "brown.categories()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['The', 'Fulton', 'County', 'Grand', 'Jury', 'said', ...]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brown.words(categories='news')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['The', 'Fulton', 'County', 'Grand', 'Jury', 'said', 'Friday', 'an', 'investigation', 'of', \"Atlanta's\", 'recent', 'primary', 'election', 'produced', '``', 'no', 'evidence', \"''\", 'that', 'any', 'irregularities', 'took', 'place', '.'], ['The', 'jury', 'further', 'said', 'in', 'term-end', 'presentments', 'that', 'the', 'City', 'Executive', 'Committee', ',', 'which', 'had', 'over-all', 'charge', 'of', 'the', 'election', ',', '``', 'deserves', 'the', 'praise', 'and', 'thanks', 'of', 'the', 'City', 'of', 'Atlanta', \"''\", 'for', 'the', 'manner', 'in', 'which', 'the', 'election', 'was', 'conducted', '.'], ...]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brown.sents(categories=['news', 'editorial', 'reviews'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cfd = nltk.ConditionalFreqDist((genre, word) for genre in brown.categories() for word in brown.words(categories=genre))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  can could   may might  must  will \n",
      "           news    93    86    66    38    50   389 \n",
      "       religion    82    59    78    12    54    71 \n",
      "        hobbies   268    58   131    22    83   264 \n",
      "science_fiction    16    49     4    12     8    16 \n",
      "        romance    74   193    11    51    45    43 \n",
      "          humor    16    30     8     8     9    13 \n"
     ]
    }
   ],
   "source": [
    "genres = ['news', 'religion', 'hobbies', 'science_fiction', 'romance', 'humor']\n",
    "modals = ['can', 'could', 'may', 'might', 'must', 'will']\n",
    "cfd.tabulate(conditions=genres, samples=modals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1, 11), (1, 12), (1, 13), (2, 11), (2, 12), (2, 13), (3, 11), (3, 12), (3, 13)]\n",
      "[(1, 12), (1, 13), (2, 13)]\n"
     ]
    }
   ],
   "source": [
    "gen = [(a,b)for a in [1,2,3] for b in [11,12,13]]\n",
    "print(gen)\n",
    "gen = [(a,b)for a in [1,2,3] for b in [11,12,13] if b > 10 + a]\n",
    "print(gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import reuters\n",
    "#reuters.fileids()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reuters.categories()\n",
    "len(reuters.categories())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import udhr\n",
    "languages = ['Chickasaw', 'English', 'German_Deutsch', 'Greenlandic_Inuktikut', 'Hungarian_Magyar', 'Ibibio_Efik']\n",
    "cfd = nltk.ConditionalFreqDist((lang, len(word)) for lang in languages for word in udhr.words(lang + '-Latin1'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  0    1    2    3    4    5    6    7    8    9 \n",
      "       English    0  185  525  883  997 1166 1283 1440 1558 1638 \n",
      "German_Deutsch    0  171  263  614  717  894 1013 1110 1213 1275 \n"
     ]
    }
   ],
   "source": [
    " cfd.tabulate(conditions=['English', 'German_Deutsch'], samples=range(10), cumulative=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 0   1   2   3   4   5   6   7   8   9 \n",
      "       English   0 185 340 358 114 169 117 157 118  80 \n",
      "German_Deutsch   0 171  92 351 103 177 119  97 103  62 \n"
     ]
    }
   ],
   "source": [
    "cfd.tabulate(conditions=['English', 'German_Deutsch'], samples=range(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with WordNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('car.n.01')]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.corpus import wordnet as wn\n",
    "wn.synsets('motorcar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['car', 'auto', 'automobile', 'machine', 'motorcar']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synset('car.n.01').lemma_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Lemma('car.n.01.car'),\n",
       " Lemma('car.n.01.auto'),\n",
       " Lemma('car.n.01.automobile'),\n",
       " Lemma('car.n.01.machine'),\n",
       " Lemma('car.n.01.motorcar')]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synset('car.n.01').lemmas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('car.n.01'),\n",
       " Synset('car.n.02'),\n",
       " Synset('car.n.03'),\n",
       " Synset('car.n.04'),\n",
       " Synset('cable_car.n.01')]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synsets('car')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['car', 'auto', 'automobile', 'machine', 'motorcar']\n",
      "['car', 'railcar', 'railway_car', 'railroad_car']\n",
      "['car', 'gondola']\n",
      "['car', 'elevator_car']\n",
      "['cable_car', 'car']\n"
     ]
    }
   ],
   "source": [
    "for synset in wn.synsets('car'):\n",
    "    print(synset.lemma_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Lemma('car.n.01.car'),\n",
       " Lemma('car.n.02.car'),\n",
       " Lemma('car.n.03.car'),\n",
       " Lemma('car.n.04.car'),\n",
       " Lemma('cable_car.n.01.car')]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.lemmas('car')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('dish.n.01'),\n",
       " Synset('dish.n.02'),\n",
       " Synset('dish.n.03'),\n",
       " Synset('smasher.n.02'),\n",
       " Synset('dish.n.05'),\n",
       " Synset('cup_of_tea.n.01'),\n",
       " Synset('serve.v.06'),\n",
       " Synset('dish.v.02')]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synsets('dish')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Lemma('dish.n.01.dish'),\n",
       " Lemma('dish.n.02.dish'),\n",
       " Lemma('dish.n.03.dish'),\n",
       " Lemma('smasher.n.02.dish'),\n",
       " Lemma('dish.n.05.dish'),\n",
       " Lemma('cup_of_tea.n.01.dish'),\n",
       " Lemma('serve.v.06.dish'),\n",
       " Lemma('dish.v.02.dish')]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.lemmas(\"dish\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "motorcar = wn.synset('car.n.01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('ambulance.n.01'),\n",
       " Synset('beach_wagon.n.01'),\n",
       " Synset('bus.n.04'),\n",
       " Synset('cab.n.03'),\n",
       " Synset('compact.n.03'),\n",
       " Synset('convertible.n.01'),\n",
       " Synset('coupe.n.01'),\n",
       " Synset('cruiser.n.01'),\n",
       " Synset('electric.n.01'),\n",
       " Synset('gas_guzzler.n.01'),\n",
       " Synset('hardtop.n.01'),\n",
       " Synset('hatchback.n.01'),\n",
       " Synset('horseless_carriage.n.01'),\n",
       " Synset('hot_rod.n.01'),\n",
       " Synset('jeep.n.01'),\n",
       " Synset('limousine.n.01'),\n",
       " Synset('loaner.n.02'),\n",
       " Synset('minicar.n.01'),\n",
       " Synset('minivan.n.01'),\n",
       " Synset('model_t.n.01'),\n",
       " Synset('pace_car.n.01'),\n",
       " Synset('racer.n.02'),\n",
       " Synset('roadster.n.01'),\n",
       " Synset('sedan.n.01'),\n",
       " Synset('sport_utility.n.01'),\n",
       " Synset('sports_car.n.01'),\n",
       " Synset('stanley_steamer.n.01'),\n",
       " Synset('stock_car.n.01'),\n",
       " Synset('subcompact.n.01'),\n",
       " Synset('touring_car.n.01'),\n",
       " Synset('used-car.n.01')]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "types_of_motorcar = motorcar.hyponyms()\n",
    "types_of_motorcar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Model_T',\n",
       " 'S.U.V.',\n",
       " 'SUV',\n",
       " 'Stanley_Steamer',\n",
       " 'ambulance',\n",
       " 'beach_waggon',\n",
       " 'beach_wagon',\n",
       " 'bus',\n",
       " 'cab',\n",
       " 'compact',\n",
       " 'compact_car',\n",
       " 'convertible',\n",
       " 'coupe',\n",
       " 'cruiser',\n",
       " 'electric',\n",
       " 'electric_automobile',\n",
       " 'electric_car',\n",
       " 'estate_car',\n",
       " 'gas_guzzler',\n",
       " 'hack',\n",
       " 'hardtop',\n",
       " 'hatchback',\n",
       " 'heap',\n",
       " 'horseless_carriage',\n",
       " 'hot-rod',\n",
       " 'hot_rod',\n",
       " 'jalopy',\n",
       " 'jeep',\n",
       " 'landrover',\n",
       " 'limo',\n",
       " 'limousine',\n",
       " 'loaner',\n",
       " 'minicar',\n",
       " 'minivan',\n",
       " 'pace_car',\n",
       " 'patrol_car',\n",
       " 'phaeton',\n",
       " 'police_car',\n",
       " 'police_cruiser',\n",
       " 'prowl_car',\n",
       " 'race_car',\n",
       " 'racer',\n",
       " 'racing_car',\n",
       " 'roadster',\n",
       " 'runabout',\n",
       " 'saloon',\n",
       " 'secondhand_car',\n",
       " 'sedan',\n",
       " 'sport_car',\n",
       " 'sport_utility',\n",
       " 'sport_utility_vehicle',\n",
       " 'sports_car',\n",
       " 'squad_car',\n",
       " 'station_waggon',\n",
       " 'station_wagon',\n",
       " 'stock_car',\n",
       " 'subcompact',\n",
       " 'subcompact_car',\n",
       " 'taxi',\n",
       " 'taxicab',\n",
       " 'tourer',\n",
       " 'touring_car',\n",
       " 'two-seater',\n",
       " 'used-car',\n",
       " 'waggon',\n",
       " 'wagon']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# synset 'car.n.01' is concept, and it is linked to various other concept through relation (i.e. hyponyms)\n",
    "# each concept i.e. synset has many synomyes which is retriviable by lemmas \n",
    "sorted(lemma.name() for synset in types_of_motorcar for lemma in synset.lemmas())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('motor_vehicle.n.01')]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "motorcar.hypernyms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('entity.n.01')]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "motorcar.root_hypernyms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('burl.n.02'),\n",
       " Synset('crown.n.07'),\n",
       " Synset('limb.n.02'),\n",
       " Synset('stump.n.01'),\n",
       " Synset('trunk.n.01')]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synset('tree.n.01').part_meronyms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('heartwood.n.01'), Synset('sapwood.n.01')]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synset('tree.n.01').substance_meronyms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('forest.n.01')]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synset('tree.n.01').member_holonyms()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Entails"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('step.v.01')]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synset('walk.v.01').entailments()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('chew.v.01'), Synset('swallow.v.01')]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synset('eat.v.01').entailments()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('arouse.v.07'), Synset('disappoint.v.01')]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synset('tease.v.03').entailments()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# antonymy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Lemma('demand.n.02.demand')]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.lemma('supply.n.02.supply').antonyms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Lemma('linger.v.04.linger')]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.lemma('rush.v.01.rush').antonyms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Lemma('inclined.a.02.inclined'), Lemma('vertical.a.01.vertical')]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.lemma('horizontal.a.01.horizontal').antonyms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# dir(wn.synset('harmony.n.02'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Semantic Similarity\n",
    "Knowing which words are semantically related is useful for indexing a collection of texts, so that a search for a general term like vehicle will match documents containing specific terms like limousine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "right = wn.synset('right_whale.n.01')\n",
    "orca = wn.synset('orca.n.01')\n",
    "minke = wn.synset('minke_whale.n.01')\n",
    "tortoise = wn.synset('tortoise.n.01')\n",
    "novel = wn.synset('novel.n.01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('baleen_whale.n.01')]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.lowest_common_hypernyms(minke)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('whale.n.02')]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.lowest_common_hypernyms(orca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('vertebrate.n.01')]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.lowest_common_hypernyms(tortoise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('entity.n.01')]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.lowest_common_hypernyms(novel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we know that whale is very specific (and baleen whale even more so), while vertebrate is more general and entity is completely general. We can quantify this concept of generality by looking up the depth of each synset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synset('baleen_whale.n.01').min_depth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synset('entity.n.01').min_depth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.path_similarity(minke)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.path_similarity(right)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.043478260869565216"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.path_similarity(novel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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