spacy_word_modo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"The sequel,Yes,Prime Minister,ran from 1986 to 1988.In total there were 38 episodes,of which all but one lasted half an hour.Almost all episodes ended with a variation of the title of the serives spoken as the answer to a question posed by the same character,Jim Hacker. Serveral episodes were adapted for BBC Radio,and a stage play was produced in 2010, the latter leading to a new television series on UKTV Gold in 2013\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load('en_core_web_sm')\n",
    "doc = nlp(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"The\"\n",
      "\"sequel\"\n",
      "\",\"\n",
      "\"Yes\"\n",
      "\",\"\n",
      "\"Prime\"\n",
      "\"Minister\"\n",
      "\",\"\n",
      "\"ran\"\n",
      "\"from\"\n",
      "\"1986\"\n",
      "\"to\"\n",
      "\"1988.In\"\n",
      "\"total\"\n",
      "\"there\"\n",
      "\"were\"\n",
      "\"38\"\n",
      "\"episodes\"\n",
      "\",\"\n",
      "\"of\"\n",
      "\"which\"\n",
      "\"all\"\n",
      "\"but\"\n",
      "\"one\"\n",
      "\"lasted\"\n",
      "\"half\"\n",
      "\"an\"\n",
      "\"hour\"\n",
      "\".\"\n",
      "\"Almost\"\n",
      "\"all\"\n",
      "\"episodes\"\n",
      "\"ended\"\n",
      "\"with\"\n",
      "\"a\"\n",
      "\"variation\"\n",
      "\"of\"\n",
      "\"the\"\n",
      "\"title\"\n",
      "\"of\"\n",
      "\"the\"\n",
      "\"serives\"\n",
      "\"spoken\"\n",
      "\"as\"\n",
      "\"the\"\n",
      "\"answer\"\n",
      "\"to\"\n",
      "\"a\"\n",
      "\"question\"\n",
      "\"posed\"\n",
      "\"by\"\n",
      "\"the\"\n",
      "\"same\"\n",
      "\"character\"\n",
      "\",\"\n",
      "\"Jim\"\n",
      "\"Hacker\"\n",
      "\".\"\n",
      "\"Serveral\"\n",
      "\"episodes\"\n",
      "\"were\"\n",
      "\"adapted\"\n",
      "\"for\"\n",
      "\"BBC\"\n",
      "\"Radio\"\n",
      "\",\"\n",
      "\"and\"\n",
      "\"a\"\n",
      "\"stage\"\n",
      "\"play\"\n",
      "\"was\"\n",
      "\"produced\"\n",
      "\"in\"\n",
      "\"2010\"\n",
      "\",\"\n",
      "\"the\"\n",
      "\"latter\"\n",
      "\"leading\"\n",
      "\"to\"\n",
      "\"a\"\n",
      "\"new\"\n",
      "\"television\"\n",
      "\"series\"\n",
      "\"on\"\n",
      "\"UKTV\"\n",
      "\"Gold\"\n",
      "\"in\"\n",
      "\"2013\"\n"
     ]
    }
   ],
   "source": [
    "for token in doc:\n",
    "    print('\"'+token.text+'\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The\t0\t7425985699627899538\tFalse\tFalse\tXxx\tDET\tDT\n",
      "sequel\t4\t9793345118106788890\tFalse\tFalse\txxxx\tNOUN\tNN\n",
      ",\t10\t2593208677638477497\tTrue\tFalse\t,\tPUNCT\t,\n",
      "Yes\t11\t12731191880880189974\tFalse\tFalse\tXxx\tINTJ\tUH\n",
      ",\t14\t2593208677638477497\tTrue\tFalse\t,\tPUNCT\t,\n",
      "Prime\t15\t11196912509850082459\tFalse\tFalse\tXxxxx\tPROPN\tNNP\n",
      "Minister\t21\t5661208895191585800\tFalse\tFalse\tXxxxx\tPROPN\tNNP\n",
      ",\t29\t2593208677638477497\tTrue\tFalse\t,\tPUNCT\t,\n",
      "ran\t30\t12767647472892411841\tFalse\tFalse\txxx\tVERB\tVBD\n",
      "from\t34\t7831658034963690409\tFalse\tFalse\txxxx\tADP\tIN\n"
     ]
    }
   ],
   "source": [
    "for token in doc[:10]:\n",
    "    print(\"{0}\\t{1}\\t{2}\\t{3}\\t{4}\\t{5}\\t{6}\\t{7}\".format(\n",
    "        token.text,\n",
    "        token.idx,\n",
    "        token.lemma,\n",
    "        token.is_punct,\n",
    "        token.is_space,\n",
    "        token.shape_,\n",
    "        token.pos_,\n",
    "        token.tag_\n",
    "    ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1986 DATE\n",
      "38 CARDINAL\n",
      "one CARDINAL\n",
      "half an hour TIME\n",
      "Jim Hacker PERSON\n",
      "BBC Radio ORG\n",
      "2010 DATE\n",
      "UKTV Gold ORG\n",
      "2013 DATE\n"
     ]
    }
   ],
   "source": [
    "for ent in doc.ents:\n",
    "    print(ent.text,ent.label_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"entities\" style=\"line-height: 2.5\">The sequel,Yes,Prime Minister,ran from \n",
       "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone\">\n",
       "    1986\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
       "</mark>\n",
       " to 1988.In total there were \n",
       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone\">\n",
       "    38\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem\">CARDINAL</span>\n",
       "</mark>\n",
       " episodes,of which all but \n",
       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone\">\n",
       "    one\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem\">CARDINAL</span>\n",
       "</mark>\n",
       " lasted \n",
       "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone\">\n",
       "    half an hour\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem\">TIME</span>\n",
       "</mark>\n",
       ".Almost all episodes ended with a variation of the title of the serives spoken as the answer to a question posed by the same character,\n",
       "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone\">\n",
       "    Jim Hacker\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
       "</mark>\n",
       ". Serveral episodes were adapted for \n",
       "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone\">\n",
       "    BBC Radio\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
       "</mark>\n",
       ",and a stage play was produced in \n",
       "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone\">\n",
       "    2010\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
       "</mark>\n",
       ", the latter leading to a new television series on \n",
       "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone\">\n",
       "    UKTV Gold\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
       "</mark>\n",
       " in \n",
       "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em; box-decoration-break: clone; -webkit-box-decoration-break: clone\">\n",
       "    2013\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
       "</mark>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from spacy import displacy\n",
    "displacy.render(doc,style='ent',jupyter = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The sequel,Yes,Prime Minister,ran from 1986 to 1988.In total there were 38 episodes,of which all but one lasted half an hour.\n",
      "Almost all episodes ended with a variation of the title of the serives spoken as the answer to a question posed by the same character,Jim Hacker.\n",
      "Serveral episodes were adapted for BBC Radio,and a stage play was produced in 2010, the latter leading to a new television series on UKTV Gold in 2013\n"
     ]
    }
   ],
   "source": [
    "for sent in doc.sents:\n",
    "    print(sent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[The sequel,Yes,Prime Minister,ran from 1986 to 1988.In total there were 38 episodes,of which all but one lasted half an hour.,\n",
       " Almost all episodes ended with a variation of the title of the serives spoken as the answer to a question posed by the same character,Jim Hacker.,\n",
       " Serveral episodes were adapted for BBC Radio,and a stage play was produced in 2010, the latter leading to a new television series on UKTV Gold in 2013]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(doc.sents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "newdoc = nlp(list(doc.sents)[0].text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The/DT<--det-- sequel/NN\n",
      "sequel/NN<--nsubj-- ran/VBD\n",
      ",/,<--punct-- sequel/NN\n",
      "Yes/UH<--intj-- sequel/NN\n",
      ",/,<--punct-- sequel/NN\n",
      "Prime/NNP<--compound-- Minister/NNP\n",
      "Minister/NNP<--appos-- sequel/NN\n",
      ",/,<--punct-- sequel/NN\n",
      "ran/VBD<--ROOT-- ran/VBD\n",
      "from/IN<--prep-- ran/VBD\n",
      "1986/CD<--pobj-- from/IN\n",
      "to/IN<--prep-- from/IN\n",
      "1988.In/CD<--nummod-- total/NN\n",
      "total/NN<--pobj-- to/IN\n",
      "there/EX<--expl-- were/VBD\n",
      "were/VBD<--ccomp-- ran/VBD\n",
      "38/CD<--nummod-- episodes/NNS\n",
      "episodes/NNS<--attr-- were/VBD\n",
      ",/,<--punct-- episodes/NNS\n",
      "of/IN<--prep-- lasted/VBD\n",
      "which/WDT<--pobj-- of/IN\n",
      "all/DT<--nsubj-- lasted/VBD\n",
      "but/CC<--cc-- all/DT\n",
      "one/CD<--conj-- all/DT\n",
      "lasted/VBD<--relcl-- episodes/NNS\n",
      "half/PDT<--predet-- hour/NN\n",
      "an/DT<--det-- hour/NN\n",
      "hour/NN<--npadvmod-- lasted/VBD\n",
      "./.<--punct-- ran/VBD\n"
     ]
    }
   ],
   "source": [
    "for token in newdoc:\n",
    "    print(\"{0}/{1}<--{2}-- {3}/{4}\".format(token.text,token.tag_,token.dep_,token.head.text,token.head.tag_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" id=\"988-0\" class=\"displacy\" width=\"2210\" height=\"452.0\" style=\"max-width: none; height: 452.0px; color: #000000; background: #ffffff; font-family: Arial\">\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"362.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"50\">The</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"50\">DET</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"362.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"140\">sequel,</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"140\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"362.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"230\">Yes,</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"230\">INTJ</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"362.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"320\">Prime</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"320\">PROPN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"362.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"410\">Minister,</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"410\">PROPN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"362.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"500\">ran</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"500\">VERB</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"362.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"590\">from</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"590\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"362.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"680\">1986</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"680\">NUM</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"362.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"770\">to</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"770\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"362.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"860\">1988.In</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"860\">NUM</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"362.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"950\">total</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"950\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"362.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1040\">there</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1040\">ADV</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"362.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1130\">were</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1130\">VERB</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"362.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1220\">38</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1220\">NUM</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"362.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1310\">episodes,</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1310\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"362.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1400\">of</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1400\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"362.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1490\">which</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1490\">ADJ</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"362.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1580\">all</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1580\">DET</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"362.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1670\">but</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1670\">CCONJ</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"362.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1760\">one</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1760\">NUM</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"362.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1850\">lasted</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1850\">VERB</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"362.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1940\">half</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1940\">ADJ</tspan>\n",
       "</text>\n",
       "\n",
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   "source": [
    "displacy.render(newdoc,style = 'dep', jupyter = True, options = {'distance':90})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      " -5.1958e-01  3.2437e-01  1.9644e-01 -1.5863e-01  1.9083e-03  5.1186e-01\n",
      " -9.5662e-02  2.1656e-02 -1.3161e-01 -1.1805e-01  1.6575e-01  2.9505e-01\n",
      "  4.9018e-01 -4.2977e-01 -1.5993e-01 -3.6753e-01  8.7457e-02 -6.8308e-02\n",
      " -1.5331e-01 -2.7103e-01 -5.4915e-01  9.1917e-01  4.2635e-01 -5.2601e-01\n",
      "  8.4366e-01  5.7226e-02  1.9534e-01 -3.8182e-01 -9.0527e-02  4.5610e-02\n",
      " -9.6872e-03 -6.0501e-01 -2.8681e-01  2.8389e-01  6.2697e-01 -1.0516e-01\n",
      " -4.8453e-01 -3.2606e-01 -5.7180e-01  4.4597e-01 -7.7640e-02  3.0929e-01\n",
      "  1.2633e-01  1.1981e+00  6.9421e-01  3.7257e-01 -4.8151e-02  8.7035e-02\n",
      " -5.7236e-01  4.3714e-01  6.7161e-02  4.1036e-01 -2.5125e-01 -6.1381e-01\n",
      "  9.6459e-03 -5.2952e-01 -2.5677e-01  2.1411e-01 -2.6532e-01  5.5811e-02\n",
      "  8.7852e-02  1.2621e-01  3.5266e-01 -5.6351e-01 -3.5930e-01 -1.3401e-01\n",
      "  7.7801e-01 -2.5249e-01  2.0385e-01 -6.5584e-01 -4.9989e-02  8.7359e-01\n",
      "  4.5170e-01 -2.5920e-01  9.2309e-01  3.6927e-01  4.7212e-01 -4.0808e-01\n",
      " -6.7676e-01  2.8296e-01  1.1870e-01  2.5123e-01  6.3988e-02  3.8518e-01\n",
      " -4.5629e-01 -4.9095e-01 -2.9056e-01  1.1609e-01 -1.4937e-01 -3.6742e-01\n",
      "  1.9663e-01  4.5344e-01 -1.3246e-01  3.2582e-01 -3.9982e-01  1.8979e-01\n",
      "  3.0114e-01  7.4952e-01  7.7731e-02  1.2773e-01  5.0207e-02  3.8849e-02\n",
      "  1.5883e-01  1.9287e-01 -4.3273e-01 -1.1075e-01  2.8002e-03  6.2109e-01\n",
      " -6.8976e-01  1.1181e-01  4.9622e-01  8.5923e-02  2.5591e-01 -1.4410e-01\n",
      "  1.6128e-01  4.0757e-01  2.0046e-01 -1.0036e-01  6.1331e-01 -5.2357e-01]\n"
     ]
    }
   ],
   "source": [
    "nlp = spacy.load('en_core_web_lg')\n",
    "print(nlp.vocab['minister'].vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dog = nlp.vocab[\"dog\"]\n",
    "cat = nlp.vocab[\"cat\"]\n",
    "apple = nlp.vocab[\"apple\"]\n",
    "orange = nlp.vocab[\"orange\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8016855"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog.similarity(cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.26339024"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog.similarity(apple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5618917"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple.similarity(orange)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.spatial.distance import cosine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8016853928565979"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1-cosine(dog.vector, cat.vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vector_similarity(x,y):\n",
    "    return 1 - cosine(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8016853928565979"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector_similarity(dog.vector,cat.vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_guess_word(words):\n",
    "    [first,second,third] = words\n",
    "    return nlp.vocab[first].vector - nlp.vocab[second].vector + nlp.vocab[third].vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_similar_word(words,scope = nlp.vocab):\n",
    "    \n",
    "    guess_word = make_guess_word(words)\n",
    "    \n",
    "    similarities = []\n",
    "    \n",
    "    for word in scope:\n",
    "        if not word.has_vector:\n",
    "            continue\n",
    "        \n",
    "        similarity = vector_similarity(guess_word, word.vector)\n",
    "        similarities.append((word, similarity))\n",
    "        \n",
    "    similarities = sorted(similarities, key = lambda item: -item[1])\n",
    "    print([word[0].text for word in similarities[:10]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "words = [\"king\",\"queen\",\"woman\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MAN', 'Man', 'mAn', 'MAn', 'MaN', 'man', 'mAN', 'WOMAN', 'womAn', 'WOman']\n"
     ]
    }
   ],
   "source": [
    "get_similar_word(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding = np.array([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_list = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "for token in doc:\n",
    "    if not(token.is_punct) and not(token.text in word_list):\n",
    "        word_list.append(token.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['The',\n",
       " 'sequel',\n",
       " 'Yes',\n",
       " 'Prime',\n",
       " 'Minister',\n",
       " 'ran',\n",
       " 'from',\n",
       " '1986',\n",
       " 'to',\n",
       " '1988.In',\n",
       " 'total',\n",
       " 'there',\n",
       " 'were',\n",
       " '38',\n",
       " 'episodes',\n",
       " 'of',\n",
       " 'which',\n",
       " 'all',\n",
       " 'but',\n",
       " 'one',\n",
       " 'lasted',\n",
       " 'half',\n",
       " 'an',\n",
       " 'hour',\n",
       " 'Almost',\n",
       " 'ended',\n",
       " 'with',\n",
       " 'a',\n",
       " 'variation',\n",
       " 'the',\n",
       " 'title',\n",
       " 'serives',\n",
       " 'spoken',\n",
       " 'as',\n",
       " 'answer',\n",
       " 'question',\n",
       " 'posed',\n",
       " 'by',\n",
       " 'same',\n",
       " 'character',\n",
       " 'Jim',\n",
       " 'Hacker',\n",
       " 'Serveral',\n",
       " 'adapted',\n",
       " 'for',\n",
       " 'BBC',\n",
       " 'Radio',\n",
       " 'and',\n",
       " 'stage',\n",
       " 'play',\n",
       " 'was',\n",
       " 'produced',\n",
       " 'in',\n",
       " '2010',\n",
       " 'latter',\n",
       " 'leading',\n",
       " 'new',\n",
       " 'television',\n",
       " 'series',\n",
       " 'on',\n",
       " 'UKTV',\n",
       " 'Gold',\n",
       " '2013']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "for word in word_list:\n",
    "    embedding = np.append(embedding, nlp.vocab[word].vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18900,)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding = embedding.reshape(len(word_list), -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(63, 300)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsne = TSNE()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "low_dim_embedding = tsne.fit_transform(embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/robert/anaconda3/lib/python3.7/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['text']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_with_labels(low_dim_embs, labels, filename = 'tsne.pdf'):\n",
    "    assert low_dim_embs.shape[0] >= len(labels), \"More labels than embeddings\"\n",
    "    plt.figure(figsize = (18,18))\n",
    "    for i, label in enumerate(labels):\n",
    "        x, y = low_dim_embs[i, :]\n",
    "        plt.scatter(x, y)\n",
    "        plt.annotate(label, xy = (x,y), xytext = (5, 2), textcoords = 'offset points', ha = 'right', va = 'bottom')\n",
    "    plt.savefig(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_with_labels(low_dim_embedding, word_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}