wordnet nltk corpus.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import wordnet as wn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "panda = wn.synset('panda.n.01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "hyper = lambda s : s.hypernyms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('procyonid.n.01'),\n",
       " Synset('carnivore.n.01'),\n",
       " Synset('placental.n.01'),\n",
       " Synset('mammal.n.01'),\n",
       " Synset('vertebrate.n.01'),\n",
       " Synset('chordate.n.01'),\n",
       " Synset('animal.n.01'),\n",
       " Synset('organism.n.01'),\n",
       " Synset('living_thing.n.01'),\n",
       " Synset('whole.n.02'),\n",
       " Synset('object.n.01'),\n",
       " Synset('physical_entity.n.01'),\n",
       " Synset('entity.n.01')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(panda.closure(hyper))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# svd vectors in python\n",
    "import numpy as np\n",
    "la = np.linalg\n",
    "\n",
    "words = [\"I\",\"like\",\"enjoy\",\"deep\",\"learning\",\"NLP\", \"flying\",\".\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "X = [[0,2,1,0,0,0,0,0],\n",
    "    [2,0,0,1,0,1,0,0],\n",
    "    [1,0,0,0,0,0,1,0],\n",
    "    [0,1,0,0,1,0,0,0],\n",
    "    [0,0,0,1,0,0,0,1],\n",
    "    [0,1,0,0,0,0,0,1],\n",
    "    [0,0,1,0,0,0,0,1],\n",
    "    [0,0,0,0,1,1,1,0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0, 2, 1, 0, 0, 0, 0, 0],\n",
       " [2, 0, 0, 1, 0, 1, 0, 0],\n",
       " [1, 0, 0, 0, 0, 0, 1, 0],\n",
       " [0, 1, 0, 0, 1, 0, 0, 0],\n",
       " [0, 0, 0, 1, 0, 0, 0, 1],\n",
       " [0, 1, 0, 0, 0, 0, 0, 1],\n",
       " [0, 0, 1, 0, 0, 0, 0, 1],\n",
       " [0, 0, 0, 0, 1, 1, 1, 0]]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -5.24124930e-01,  -5.72859145e-01,   9.54463014e-02,\n",
       "          3.83228493e-01,  -1.76963375e-01,  -1.76092183e-01,\n",
       "         -4.19185600e-01,  -5.57702732e-02],\n",
       "       [ -5.94438071e-01,   6.30120664e-01,  -1.70207779e-01,\n",
       "          3.10038363e-01,   1.84062339e-01,  -2.34777849e-01,\n",
       "          1.29535474e-01,   1.36813128e-01],\n",
       "       [ -2.56274005e-01,   2.74017533e-01,   1.59810848e-01,\n",
       "         -1.89835915e-16,  -5.78984617e-01,   6.36550929e-01,\n",
       "         -4.31598690e-16,  -3.05414877e-01],\n",
       "       [ -2.85637408e-01,  -2.47912130e-01,   3.54610324e-01,\n",
       "         -7.31901294e-02,   4.45784489e-01,   8.36141432e-02,\n",
       "          5.48721075e-01,  -4.68012411e-01],\n",
       "       [ -1.93139313e-01,   3.38495048e-02,  -5.00790405e-01,\n",
       "         -4.28462480e-01,   3.47110226e-01,   1.55483227e-01,\n",
       "         -4.68663749e-01,  -4.03576557e-01],\n",
       "       [ -3.05134684e-01,  -2.93988990e-01,  -2.23433593e-01,\n",
       "         -1.91614246e-01,   1.27460940e-01,   4.91219408e-01,\n",
       "          2.09592800e-01,   6.57535375e-01],\n",
       "       [ -1.82489837e-01,  -1.61027767e-01,  -3.97842428e-01,\n",
       "         -3.83228493e-01,  -5.12923221e-01,  -4.27574417e-01,\n",
       "          4.19185600e-01,  -1.18313828e-01],\n",
       "       [ -2.46898426e-01,   1.57254762e-01,   5.92991677e-01,\n",
       "         -6.20076727e-01,  -3.21868120e-02,  -2.31065080e-01,\n",
       "         -2.59070949e-01,   2.37976916e-01]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U ,s, Vh = la.svd(X, full_matrices=False)\n",
    "U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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swZw5c1ZYv3TpUmbPns3EiRMZO3Ys06ZN47bbbluhzu23384+++zDmDFj2GqrrZg5cyZL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\nXAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x240f16a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "for i in xrange(len(words)):\n",
    "    plt.text(-U[i,0],-U[i,1],words[i])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zIhAciGX3eVU9IMnJSZ6ayZ3u75jk5VV1RXfb52unpl/3+e+oQAAs0j8nuT6T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\nl8iY+yUiSSEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22e85908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}