eight_WordNet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import wordnet as wn "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('dog.n.01'),\n",
       " Synset('frump.n.01'),\n",
       " Synset('dog.n.03'),\n",
       " Synset('cad.n.01'),\n",
       " Synset('frank.n.02'),\n",
       " Synset('pawl.n.01'),\n",
       " Synset('andiron.n.01'),\n",
       " Synset('chase.v.01')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synsets('dog')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cad', 'bounder', 'blackguard', 'dog', 'hound', 'heel']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synset('dog.n.04').lemma_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('cad.n.01'), Synset('computer-aided_design.n.01')]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synsets('cad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Lemma('dog.n.01.dog'),\n",
       " Lemma('dog.n.01.domestic_dog'),\n",
       " Lemma('dog.n.01.Canis_familiaris')]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synset('dog.n.01').lemmas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Synset('dog.n.01')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.lemma('dog.n.01.domestic_dog').synset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Synset('dog.n.01') :  ['dog', 'domestic_dog', 'Canis_familiaris']\n",
      "Synset('frump.n.01') :  ['frump', 'dog']\n",
      "Synset('dog.n.03') :  ['dog']\n",
      "Synset('cad.n.01') :  ['cad', 'bounder', 'blackguard', 'dog', 'hound', 'heel']\n",
      "Synset('frank.n.02') :  ['frank', 'frankfurter', 'hotdog', 'hot_dog', 'dog', 'wiener', 'wienerwurst', 'weenie']\n",
      "Synset('pawl.n.01') :  ['pawl', 'detent', 'click', 'dog']\n",
      "Synset('andiron.n.01') :  ['andiron', 'firedog', 'dog', 'dog-iron']\n",
      "Synset('chase.v.01') :  ['chase', 'chase_after', 'trail', 'tail', 'tag', 'give_chase', 'dog', 'go_after', 'track']\n"
     ]
    }
   ],
   "source": [
    "for synset in wn.synsets('dog'):\n",
    "    print(synset, \": \", synset.lemma_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a member of the genus Canis (probably descended from the common wolf) that has been domesticated by man since prehistoric times; occurs in many breeds'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synset('dog.n.01').definition()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['the dog barked all night']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synset('dog.n.01').examples()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Synset('dog.n.01') :   a member of the genus Canis (probably descended from the common wolf) that has been domesticated by man since prehistoric times; occurs in many breeds\n",
      "Synset('frump.n.01') :   a dull unattractive unpleasant girl or woman\n",
      "Synset('dog.n.03') :   informal term for a man\n",
      "Synset('cad.n.01') :   someone who is morally reprehensible\n",
      "Synset('frank.n.02') :   a smooth-textured sausage of minced beef or pork usually smoked; often served on a bread roll\n",
      "Synset('pawl.n.01') :   a hinged catch that fits into a notch of a ratchet to move a wheel forward or prevent it from moving backward\n",
      "Synset('andiron.n.01') :   metal supports for logs in a fireplace\n",
      "Synset('chase.v.01') :   go after with the intent to catch\n"
     ]
    }
   ],
   "source": [
    "for synset in wn.synsets('dog'):\n",
    "    print (synset, \":  \", synset.definition())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Synset('dog.n.01') :  \n",
      "      ['dog', 'domestic_dog', 'Canis_familiaris']\n",
      "      a member of the genus Canis (probably descended from the common wolf) that has been domesticated by man since prehistoric times; occurs in many breeds\n",
      "      ['the dog barked all night']\n",
      "Synset('frump.n.01') :  \n",
      "      ['frump', 'dog']\n",
      "      a dull unattractive unpleasant girl or woman\n",
      "      ['she got a reputation as a frump', \"she's a real dog\"]\n",
      "Synset('dog.n.03') :  \n",
      "      ['dog']\n",
      "      informal term for a man\n",
      "      ['you lucky dog']\n",
      "Synset('cad.n.01') :  \n",
      "      ['cad', 'bounder', 'blackguard', 'dog', 'hound', 'heel']\n",
      "      someone who is morally reprehensible\n",
      "      ['you dirty dog']\n",
      "Synset('frank.n.02') :  \n",
      "      ['frank', 'frankfurter', 'hotdog', 'hot_dog', 'dog', 'wiener', 'wienerwurst', 'weenie']\n",
      "      a smooth-textured sausage of minced beef or pork usually smoked; often served on a bread roll\n",
      "      []\n",
      "Synset('pawl.n.01') :  \n",
      "      ['pawl', 'detent', 'click', 'dog']\n",
      "      a hinged catch that fits into a notch of a ratchet to move a wheel forward or prevent it from moving backward\n",
      "      []\n",
      "Synset('andiron.n.01') :  \n",
      "      ['andiron', 'firedog', 'dog', 'dog-iron']\n",
      "      metal supports for logs in a fireplace\n",
      "      ['the andirons were too hot to touch']\n",
      "Synset('chase.v.01') :  \n",
      "      ['chase', 'chase_after', 'trail', 'tail', 'tag', 'give_chase', 'dog', 'go_after', 'track']\n",
      "      go after with the intent to catch\n",
      "      ['The policeman chased the mugger down the alley', 'the dog chased the rabbit']\n"
     ]
    }
   ],
   "source": [
    "for synset in wn.synsets('dog'):\n",
    "\n",
    "\tprint (synset, \":  \")\n",
    "\tprint ('     ', synset.lemma_names())\n",
    "\tprint ('     ', synset.definition())\n",
    "\tprint ('     ', synset.examples())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('canine.n.02'), Synset('domestic_animal.n.01')]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog1 = wn.synset('dog.n.01')\n",
    "\n",
    "dog1.hypernyms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('entity.n.01')]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog1.root_hypernyms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('basenji.n.01'),\n",
       " Synset('corgi.n.01'),\n",
       " Synset('cur.n.01'),\n",
       " Synset('dalmatian.n.02'),\n",
       " Synset('great_pyrenees.n.01'),\n",
       " Synset('griffon.n.02'),\n",
       " Synset('hunting_dog.n.01'),\n",
       " Synset('lapdog.n.01'),\n",
       " Synset('leonberg.n.01'),\n",
       " Synset('mexican_hairless.n.01'),\n",
       " Synset('newfoundland.n.01'),\n",
       " Synset('pooch.n.01'),\n",
       " Synset('poodle.n.01'),\n",
       " Synset('pug.n.01'),\n",
       " Synset('puppy.n.01'),\n",
       " Synset('spitz.n.01'),\n",
       " Synset('toy_dog.n.01'),\n",
       " Synset('working_dog.n.01')]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog1.hyponyms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('flag.n.07')]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog1.part_meronyms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a conspicuously marked or shaped tail'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synset('flag.n.07').definition()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synset('flag.n.07').examples()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "good1 = wn.synset('good.a.01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['good']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "good1.lemma_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Lemma('good.a.01.good')]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "good1.lemmas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Lemma('bad.a.01.bad')]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "good1.lemmas()[0].antonyms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "strange = wn.synset('strange.a.01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['strange', 'unusual']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strange.lemma_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Lemma('strange.a.01.strange'), Lemma('strange.a.01.unusual')]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strange.lemmas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Lemma('familiar.a.02.familiar')]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strange.lemmas()[0].antonyms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('step.v.01')]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synset('walk.v.01').entailments()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synset('move.v.01').entailments()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = dog1.hypernym_paths()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['entity.n.01',\n",
       " 'physical_entity.n.01',\n",
       " 'object.n.01',\n",
       " 'whole.n.02',\n",
       " 'living_thing.n.01',\n",
       " 'organism.n.01',\n",
       " 'animal.n.01',\n",
       " 'chordate.n.01',\n",
       " 'vertebrate.n.01',\n",
       " 'mammal.n.01',\n",
       " 'placental.n.01',\n",
       " 'carnivore.n.01',\n",
       " 'canine.n.02',\n",
       " 'dog.n.01']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[synset.name() for synset in path[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['entity.n.01',\n",
       " 'physical_entity.n.01',\n",
       " 'object.n.01',\n",
       " 'whole.n.02',\n",
       " 'living_thing.n.01',\n",
       " 'organism.n.01',\n",
       " 'animal.n.01',\n",
       " 'domestic_animal.n.01',\n",
       " 'dog.n.01']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[synset.name() for synset in path[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "right = wn.synset('right_whale.n.01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "orca = wn.synset('orca.n.01') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "minke = wn.synset('minke_whale.n.01')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[Synset('entity.n.01'),\n",
       "  Synset('physical_entity.n.01'),\n",
       "  Synset('object.n.01'),\n",
       "  Synset('whole.n.02'),\n",
       "  Synset('living_thing.n.01'),\n",
       "  Synset('organism.n.01'),\n",
       "  Synset('animal.n.01'),\n",
       "  Synset('chordate.n.01'),\n",
       "  Synset('vertebrate.n.01'),\n",
       "  Synset('mammal.n.01'),\n",
       "  Synset('placental.n.01'),\n",
       "  Synset('aquatic_mammal.n.01'),\n",
       "  Synset('cetacean.n.01'),\n",
       "  Synset('whale.n.02'),\n",
       "  Synset('baleen_whale.n.01'),\n",
       "  Synset('right_whale.n.01')]]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.hypernym_paths()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[Synset('entity.n.01'),\n",
       "  Synset('physical_entity.n.01'),\n",
       "  Synset('object.n.01'),\n",
       "  Synset('whole.n.02'),\n",
       "  Synset('living_thing.n.01'),\n",
       "  Synset('organism.n.01'),\n",
       "  Synset('animal.n.01'),\n",
       "  Synset('chordate.n.01'),\n",
       "  Synset('vertebrate.n.01'),\n",
       "  Synset('mammal.n.01'),\n",
       "  Synset('placental.n.01'),\n",
       "  Synset('aquatic_mammal.n.01'),\n",
       "  Synset('cetacean.n.01'),\n",
       "  Synset('whale.n.02'),\n",
       "  Synset('baleen_whale.n.01'),\n",
       "  Synset('rorqual.n.01'),\n",
       "  Synset('lesser_rorqual.n.01')]]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minke.hypernym_paths()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[Synset('entity.n.01'),\n",
       "  Synset('physical_entity.n.01'),\n",
       "  Synset('object.n.01'),\n",
       "  Synset('whole.n.02'),\n",
       "  Synset('living_thing.n.01'),\n",
       "  Synset('organism.n.01'),\n",
       "  Synset('animal.n.01'),\n",
       "  Synset('chordate.n.01'),\n",
       "  Synset('vertebrate.n.01'),\n",
       "  Synset('mammal.n.01'),\n",
       "  Synset('placental.n.01'),\n",
       "  Synset('aquatic_mammal.n.01'),\n",
       "  Synset('cetacean.n.01'),\n",
       "  Synset('whale.n.02'),\n",
       "  Synset('toothed_whale.n.01'),\n",
       "  Synset('dolphin.n.02'),\n",
       "  Synset('killer_whale.n.01')]]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orca.hypernym_paths()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('baleen_whale.n.01')]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.lowest_common_hypernyms(minke)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.min_depth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synset('baleen_whale.n.01').min_depth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synset('entity.n.01').min_depth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.path_similarity(minke)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16666666666666666"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.path_similarity(orca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "novel= wn.synset('novel.n.01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.043478260869565216"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.path_similarity(novel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.845826690498331"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.lch_similarity(orca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5020919437972361"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.lch_similarity(novel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import wordnet_ic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "brown_ic = wordnet_ic.ic('ic-brown.dat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.939239388446227"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.res_similarity(orca, brown_ic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.res_similarity(novel, brown_ic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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  "language_info": {
   "codemirror_mode": {
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