NLP-Wordnet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wordnet"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "WordNet is a lexical database for the English language,\n",
    "which was created by Princeton, and is part of the NLTK corpus.\n",
    "You can use WordNet alongside the NLTK module to find the meanings of \n",
    "words, synonyms, antonyms, and more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import wordnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "syns = wordnet.synsets(\"program\")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "It groups English words into sets of synonyms called synsets, provides short definitions and usage examples, \n",
    "and records a number of relations among these synonym sets or their members"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(syns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('plan.n.01'),\n",
       " Synset('program.n.02'),\n",
       " Synset('broadcast.n.02'),\n",
       " Synset('platform.n.02'),\n",
       " Synset('program.n.05'),\n",
       " Synset('course_of_study.n.01'),\n",
       " Synset('program.n.07'),\n",
       " Synset('program.n.08'),\n",
       " Synset('program.v.01'),\n",
       " Synset('program.v.02')]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "syns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'plan.n.01'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "syns[0].name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'plan'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(syns[0].lemmas())[0].name()  #Just print the word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a series of steps to be carried out or goals to be accomplished'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "syns[0].definition() #definition of the syn set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['they drew up a six-step plan', 'they discussed plans for a new bond issue']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "syns[0].examples()  # Print examples of the synset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "synonyms = []\n",
    "antonyms = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    " for syn in wordnet.synsets(\"good\"):\n",
    "        for i in syn.lemmas():\n",
    "            synonyms.append(i.name())\n",
    "            if i.antonyms():\n",
    "                antonyms.append(i.antonyms()[0].name())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['evil', 'evilness', 'bad', 'badness', 'bad', 'evil', 'ill']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "antonyms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'good'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((wordnet.synsets(\"good\")[0].lemmas())[0].name())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((wordnet.synsets(\"good\")[0].lemmas())[0].antonyms())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9090909090909091\n"
     ]
    }
   ],
   "source": [
    "w1 = wordnet.synset('ship.n.01')\n",
    "w2 = wordnet.synset('boat.n.01')\n",
    "print(w1.wup_similarity(w2))#similarity between words by using wordnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6956521739130435\n"
     ]
    }
   ],
   "source": [
    "w1 = wordnet.synset('ship.n.01')\n",
    "w2 = wordnet.synset('car.n.01')\n",
    "print(w1.wup_similarity(w2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.32\n"
     ]
    }
   ],
   "source": [
    "w1 = wordnet.synset('ship.n.01')\n",
    "w2 = wordnet.synset('cat.n.01')\n",
    "print(w1.wup_similarity(w2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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