ProtoA_improved.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy\n",
    "from spacy import displacy\n",
    "import pickle\n",
    "activated = spacy.prefer_gpu()\n",
    "nlp = spacy.load('en_core_web_lg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_case = [\n",
    "    \"Understand customer needs and develop high-quality presentations, proposals and software demonstrations that speak to these needs - building a consensus for change on a multi-stakeholder basis\",\n",
    "    \"Work with the Enterprise sales team to build, manage and maintain customer relationships\",\n",
    "    \"Need to understand and demonstrate the solution and how to integrate to other business systems\",\n",
    "    \"Understanding and experience within KPIs, identifying data sources, building a visualization map and dashboarding.\",\n",
    "    \"Define, guide and implement innovation programs' governance, portfolio mix and business fit\",\n",
    "    \"Establish the use of cloud services as a means to drive innovation\",\n",
    "    \"Collaborate with other AWS specialist teams to drive and shape solutions to accelerate our customer's business outcomes\",\n",
    "    \"Experience with a major consulting firm or experience in a corporate role delivering strategy and operations projects with an emphasis in business transformation at the intersection of new operating models and new digital technologies.\",\n",
    "    \"Understanding of modern cloud delivery models (Public, Private and Hybrid).\",\n",
    "    \"Excellent program management skills including developing project plans, resourcing and budgeting projects, and managing a disciplined execution methodology with both internal (direct) and external (indirect) team members.\",\n",
    "    \"Experience in two or more of the following transformation practice areas: ERP (business process architecture and solution implementation), business case and use case development, financial modelling, BI and Analytics strategy and implementation, complex program management, enterprise business / process / IT architectures, executive agenda items and metrics.\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M5845,791.5 L5837,779.5 5853,779.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M5985.0,791.5 L5993.0,779.5 5977.0,779.5\" fill=\"currentColor\"/>\n",
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       "\n",
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       "\n",
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       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M6370,791.5 L6362,779.5 6378,779.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
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       "        <textPath xlink:href=\"#arrow-220f77af40924d6c8d468bb5a8c98ef5-0-36\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">nmod</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M6545,791.5 L6537,779.5 6553,779.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-220f77af40924d6c8d468bb5a8c98ef5-0-37\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">compound</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M6720,791.5 L6712,779.5 6728,779.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-220f77af40924d6c8d468bb5a8c98ef5-0-38\" stroke-width=\"2px\" d=\"M6020,789.5 C6020,352.0 6855.0,352.0 6855.0,789.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-220f77af40924d6c8d468bb5a8c98ef5-0-38\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">conj</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M6855.0,791.5 L6863.0,779.5 6847.0,779.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-220f77af40924d6c8d468bb5a8c98ef5-0-39\" stroke-width=\"2px\" d=\"M7070,789.5 C7070,614.5 7365.0,614.5 7365.0,789.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-220f77af40924d6c8d468bb5a8c98ef5-0-39\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">amod</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M7070,791.5 L7062,779.5 7078,779.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-220f77af40924d6c8d468bb5a8c98ef5-0-40\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">compound</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M7245,791.5 L7237,779.5 7253,779.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M7715.0,791.5 L7723.0,779.5 7707.0,779.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "</svg></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Base =====  Experience in two or more of the following transformation practice areas: ERP business process architecture and solution implementation, business case and use case development, financial modelling, BI and Analytics strategy and implementation, complex program management, enterprise business / process / IT architectures, executive agenda items and metrics.  =======\n",
      "\n",
      "use case development\n",
      "use financial modelling\n",
      "use BI and Analytics strategy\n",
      "use implementation\n",
      "use enterprise business process IT architectures\n",
      "use executive agenda items\n",
      "use metrics\n",
      "Experience in more of the following transformation practice areas ERP business process architecture business case and\n",
      "Experience in more of the following transformation practice areas solution implementation business case and\n",
      "Experience in two of the following transformation practice areas ERP business process architecture business case and\n",
      "Experience in two of the following transformation practice areas solution implementation business case and\n"
     ]
    }
   ],
   "source": [
    "# preprocess such as remove parenthesis\n",
    "def preprocess(text):\n",
    "    return text.replace(\"(\",\"\").replace(\")\", \"\")\n",
    "\n",
    "# Split VP phrases from the sentence\n",
    "def split_VP_phrases(doc):\n",
    "    print(\"Base ===== \", doc.text, \" =======\\n\")\n",
    "    res = []\n",
    "    root = doc[:].root\n",
    "    \n",
    "    visited = [False for _ in doc ]\n",
    "    conj_list = [None for _ in doc]\n",
    "    \n",
    "    def convert(x):\n",
    "        return x\n",
    "    \n",
    "    # check if rt is conjunction\n",
    "    def check_conj(rt):\n",
    "        if conj_list[rt.i] != None:\n",
    "            return conj_list[rt.i]\n",
    "        if rt.i < rt.head.i:\n",
    "            conj_list[rt.i] = (False, None)\n",
    "            return False, None\n",
    "        for token in rt.rights:\n",
    "            if rt.pos_ != 'VERB' and token.dep_ not in ['cc', 'conj', 'punct']:\n",
    "                conj_list[rt.i] = (False, None)\n",
    "                return False, None\n",
    "            if token.dep_ == 'conj':\n",
    "                conj_list[rt.i] = (True, token)\n",
    "                return True, token\n",
    "        for token in rt.head.rights:\n",
    "            if token.dep_ in ['ccomp', 'xcomp'] and rt.i < token.i:\n",
    "                token.dep_ = 'conj'\n",
    "                conj_list[rt.i] = (True, token)\n",
    "                return True, token\n",
    "        conj_list[rt.i] = (False, None)\n",
    "        return False, None\n",
    "    \n",
    "    # check if rt is the root of VP(verb phrase)\n",
    "    def check_split_verb(rt):\n",
    "        if rt.pos_ != 'VERB':\n",
    "            return False\n",
    "        if rt.dep_ == 'conj':\n",
    "            if rt.head.pos_ != 'VERB':\n",
    "                return True\n",
    "            return check_split_verb(rt.head)\n",
    "        if rt.dep_ in ['xcomp', 'ccomp', 'advcl', 'nsubj', 'acl', 'relcl', 'prep', 'pcomp']:\n",
    "            return False\n",
    "        return True\n",
    "    \n",
    "    # check if rt is the punction\n",
    "    def check_punct(rt):\n",
    "        if rt.pos_ == 'PUNCT' or rt.pos_ == 'SYM':\n",
    "            return True\n",
    "        if rt.pos_ == 'CCONJ':\n",
    "            is_conj, _ = check_conj(rt.head)\n",
    "            return is_conj\n",
    "        return False\n",
    "    \n",
    "    # split to phrases\n",
    "    def split(rt):  \n",
    "        visited[rt.i] = True\n",
    "    \n",
    "        if check_punct(rt):\n",
    "            return []\n",
    "        \n",
    "        ret = ['']\n",
    "        if not check_split_verb(rt) or rt == root:\n",
    "            for token in rt.lefts:\n",
    "                left_ret = split(token)\n",
    "                if len(left_ret) == 0:\n",
    "                    continue\n",
    "                ret_ = [x + y + \" \" for x in ret for y in left_ret]\n",
    "                ret = ret_\n",
    "     \n",
    "        ret_l = ret\n",
    "        ret_ = [x + rt.text for x in ret]\n",
    "        ret = []\n",
    "     \n",
    "        flag = False\n",
    "        extra_flag = False\n",
    "        is_conj, conj = check_conj(rt)\n",
    "        \n",
    "        rights = [tk for tk in rt.rights]\n",
    "        if is_conj and not conj in rights:\n",
    "            rights.append(conj)\n",
    "        \n",
    "        for token in rights:\n",
    "            if (visited[token.i]):\n",
    "                continue\n",
    "            right_ret = split(token)\n",
    "            if len(right_ret) == 0:\n",
    "                continue\n",
    "            \n",
    "            if is_conj and token.dep_ == 'conj':\n",
    "                if not flag and token.pos_ == rt.pos_:\n",
    "                    if not extra_flag:\n",
    "                        for sent in right_ret:\n",
    "                            right = doc[token.left_edge.i : token.i+1].text\n",
    "                            if right in sent:\n",
    "                                ret.extend([sent.replace(right, x) for x in ret_])\n",
    "                    flag = True\n",
    "                if not check_split_verb(token):\n",
    "                    if rt.pos_ == 'VERB':\n",
    "                        ret.extend([x + y for x in ret_l for y in right_ret])\n",
    "                    else:\n",
    "                        ret.extend(right_ret)\n",
    "                if not flag and token == conj:\n",
    "                    ret.extend(ret_)\n",
    "                ret_ = ret\n",
    "               \n",
    "            else:\n",
    "                extra_flag = True\n",
    "                ret = [x + \" \" + y for x in ret_ for y in right_ret]\n",
    "                ret_ = ret\n",
    "       \n",
    "        ret = ret_\n",
    "        \n",
    "        if check_split_verb(rt):\n",
    "            res.extend(ret)\n",
    "            is_conj, conj = check_conj(rt.head)\n",
    "            if is_conj and conj == rt:\n",
    "                return ret\n",
    "            else:\n",
    "                return []\n",
    "        else:\n",
    "            if rt.i == root.i:\n",
    "                res.extend(ret)\n",
    "            return ret\n",
    "        \n",
    "    split(root)\n",
    "    \n",
    "    # Normalize the sentence\n",
    "    res_ = []\n",
    "    for sent in res:\n",
    "        sent = nlp(sent)\n",
    "        cnt = 0\n",
    "        for token in sent:\n",
    "            if token.pos_ in ['VERB', 'NOUN']:\n",
    "                cnt += 1\n",
    "        if cnt < 2:\n",
    "            continue    \n",
    "        res_.append(sent.text)\n",
    "    res = convert(res_)\n",
    "    print('\\n'.join(res))\n",
    "    return res\n",
    "\n",
    "ex = nlp(preprocess(test_case[10]))\n",
    "\n",
    "from spacy import displacy\n",
    "displacy.render(ex, style = \"dep\", jupyter = True)\n",
    "ret = split_VP_phrases(ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROOT\n",
      "cc\n",
      "conj\n",
      "prep\n",
      "pobj\n",
      "punct\n",
      "acl\n",
      "compound\n",
      "dobj\n",
      "punct\n",
      "conj\n",
      "det\n",
      "compound\n",
      "dobj\n",
      "cc\n",
      "conj\n"
     ]
    }
   ],
   "source": [
    "for token in ex:\n",
    "    print(token.dep_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'adposition'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spacy.explain(\"ADP\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import textacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "example = \"Textacy is a Python library for performing higher-level natural language processing (NLP) tasks, built on the high-performance Spacy library. With the basics — tokenization, part-of-speech tagging, parsing — offloaded to another library, textacy focuses on tasks facilitated by the availability of tokenized, POS-tagged, and parsed text: keyterm extraction, readability statistics, emotional valence analysis, quotation attribution, and more.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_text = \"\"\"Experience in two or more of the following transformation practice areas: ERP (business process architecture and solution implementation), business case and use case development, financial modelling, BI and Analytics strategy and implementation, complex program management, enterprise business / process / IT architectures, executive agenda items and metrics.\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Experience in two or more of the following transformation practice areas: ERP (business process architecture and solution implementation), business case and use case development, financial modelling, BI and Analytics strategy and implementation, complex program management, enterprise business / process / IT architectures, executive agenda items and metrics.'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc = textacy.make_spacy_doc(raw_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from textacy import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Doc(57 tokens: \"Experience in two or more of the following tran...\")'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc._.preview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<DET>? <NUM>* (<ADJ> <PUNCT>? <CONJ>?)* (<NOUN>|<PROPN> <PART>?)+'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pattern = textacy.constants.POS_REGEX_PATTERNS[\"en\"][\"NP\"]\n",
    "pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = textacy.TextStats(doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'flesch_kincaid_grade_level': 27.395555555555564,\n",
       " 'flesch_reading_ease': -21.19999999999996,\n",
       " 'smog_index': 25.98008509911553,\n",
       " 'gunning_fog_index': 32.22222222222222,\n",
       " 'coleman_liau_index': 22.871370911111114,\n",
       " 'automated_readability_index': 32.574666666666666,\n",
       " 'lix': 100.55555555555556,\n",
       " 'gulpease_index': 28.77777777777778,\n",
       " 'wiener_sachtextformel': 19.281222222222222}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.readability_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_sents': 1,\n",
       " 'n_words': 45,\n",
       " 'n_chars': 301,\n",
       " 'n_syllables': 97,\n",
       " 'n_unique_words': 36,\n",
       " 'n_long_words': 25,\n",
       " 'n_monosyllable_words': 20,\n",
       " 'n_polysyllable_words': 16}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.basic_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'concept_net',\n",
       " 'site_url': 'http://conceptnet.io',\n",
       " 'publication_url': 'https://arxiv.org/abs/1612.03975',\n",
       " 'description': 'An open, multilingual semantic network of general knowledge, designed to help computers understand the meanings of words.'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import textacy.resources\n",
    "rs = textacy.resources.ConceptNet()\n",
    "rs.download()\n",
    "rs.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "757it [00:00, 84333.93it/s]\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "not enough values to unpack (expected 5, got 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-1a3d2c2696b4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mrs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_antonyms\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"go\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlang\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"en\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msense\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"v\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\textacy\\resources\\concept_net.py\u001b[0m in \u001b[0;36mget_antonyms\u001b[1;34m(self, term, lang, sense)\u001b[0m\n\u001b[0;32m    279\u001b[0m         \u001b[0mReturns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    280\u001b[0m             \u001b[0mList\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 281\u001b[1;33m         \"\"\"\n\u001b[0m\u001b[0;32m    282\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_relation_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mantonyms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mterm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlang\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlang\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msense\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msense\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    283\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\textacy\\resources\\concept_net.py\u001b[0m in \u001b[0;36mantonyms\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    264\u001b[0m         \u001b[0mBased\u001b[0m \u001b[0mon\u001b[0m \u001b[0mthe\u001b[0m \u001b[1;34m\"/r/Antonym\"\u001b[0m \u001b[0mrelation\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mConceptNet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    265\u001b[0m         \"\"\"\n\u001b[1;32m--> 266\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_antonyms\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    267\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_antonyms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_relation_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"/r/Antonym\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mis_symmetric\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    268\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_antonyms\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\textacy\\resources\\concept_net.py\u001b[0m in \u001b[0;36m_get_relation_data\u001b[1;34m(self, relation, is_symmetric)\u001b[0m\n\u001b[0;32m    170\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrows\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    171\u001b[0m                     \u001b[0mpbar\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 172\u001b[1;33m                     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    173\u001b[0m                     \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrel_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstart_uri\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend_uri\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrow\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    174\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[0mrel_type\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mrelation\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: not enough values to unpack (expected 5, got 4)"
     ]
    }
   ],
   "source": [
    "rs.get_antonyms(\"go\", lang=\"en\", sense=\"v\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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