KG for unstructured text.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# t=open('Indian_Fiction_in_English_-_Course_Introduction_1_.txt','r')\n",
    "t=open('Indian Fiction In English Course Introduction.txt','r')\n",
    "# t=open('AI ML.txt','r')\n",
    "with t:\n",
    "    txt=t.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Hello everyone',\n",
       " ' Thank you for your interest in this NPTEL course titled Indian Fiction in English',\n",
       " ' And Dr Merin Simi Raj, I teach in the department of humanities and social sciences at IIT Madras',\n",
       " ' To give you a brief overview of this course this is a 12-week course that spread across 30 hours of teaching',\n",
       " ' This has been pitched at the post graduate level',\n",
       " ' I must also tell you that we have been offering this course in the department of humanities and social sciences at IIT Madras for the last of four years and the course has been conceived in such a way that it also generates research interests and students who have interest in highest studies and also on students who have an interest in honouring their research Acumen, there are no prerequisites for this',\n",
       " ' I would have ever look forward to for a lot of readiness from your side to learn and also willingness to read',\n",
       " ' Apart from that, I also need to reiterate what course does not do, here we will not be providing some reason particular text',\n",
       " ' This is not a course on reading stories and storytelling on the other hand we shall be looking at various Frameworks and introducing you to a new way of looking at it this field of study Indian Fiction in English',\n",
       " ' One of the first things that we shall be doing as a part of this course is to unpack this a cat video of Indian Fiction in English',\n",
       " ' Traditionally as the term implies you are aware that we are talking about a body of writing produced by authors who were either born in India or lived in India or are of Indian origin',\n",
       " ' We look at one particular genre, that is fiction which comprises of novels and short stories and we are looking at the body of fiction written particularly in English language and since we are not to looking at the works translated from other languages into English either',\n",
       " ' So we will be unpacking this category and try to interrogate the various contestations and the various definitions that are available when terms of authorship study and in terms of looking at different kinds of genres and also talking about issues related to language and identity',\n",
       " ' We will be approaching this course from three major angles',\n",
       " ' We should first be looking at the historical context and looking at the certain canonical text which of laid the foundations of the chronological and historical origins of this genre',\n",
       " 'We will be looking at the critical tradition, which has been formulated by writers such as Meenakshi Mukherjee, Priyamvada Gopalan and John mee',\n",
       " ' We shall also be looking at the original text, the original fiction work produced from the 19th century onwards',\n",
       " 'We shall be focusing on to other major phases in 1930s in the post 1980s',\n",
       " \" Overall this is an attempt to cover a vast literary ground from the mid-nineteenth century till the recent part so in that sense we shall be looking at the first novel written in English published in 1864 and written by Bankim Chandra Chatterjee, the novel Rajmohan's wife\",\n",
       " ' And we hall be ending the discussion with recent novel published by Allan Sealy in 2017 titled Zelaldinus',\n",
       " ' So this vast literary ground will not covered through summaries and analyisis',\n",
       " ' On the contrary, we shall be looking a particular frameworks, which have informed the understanding of the genre and also the formulation of the critical tradition as far as this discipline is concerened',\n",
       " 'We shall be looking at a range of things from colonial modernity which informs he origins of this genre and also how the histroy and the storytelling has been progressing vis-a-vis nationalist histrography',\n",
       " ' We shall be looking of the major frameworks of the 20th century which have infomreed the understanding of the genre itself such as post-colonialism',\n",
       " ' Our pespectives will be enabled by the ideas of gender and caste',\n",
       " ' We will be looking at how the literary market influence the formulations of this genre',\n",
       " ' We shall also be looking at inherent elitism which is a part of the genre as well as writing in English',\n",
       " ' I hope this course, Indian Fiction in English will be of much use to you',\n",
       " \" If you're a student of English or if you are someone who has got general interest in the idea of Indian writings in English,you're welcome to join this course\",\n",
       " ' I wish you a really fruitful learning experience and I look forward to you joining this course',\n",
       " ' Thank you so much for your attention and Thank you for listening to this video',\n",
       " '']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "txt.split(\".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "f=open('TextOutput.csv','w+')\n",
    "with f:\n",
    "    writer = csv.writer(f)\n",
    "    writer.writerow([\"sentence\"])\n",
    "    for i in txt.split(\".\"):\n",
    "        if i != \"\":\n",
    "            writer.writerow([i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "textOP=pd.read_csv('TextOutput.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hello everyone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Thank you for your interest in this NPTEL cou...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>And Dr Merin Simi Raj, I teach in the departm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>To give you a brief overview of this course t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>This has been pitched at the post graduate level</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>I must also tell you that we have been offeri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>I would have ever look forward to for a lot o...</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Apart from that, I also need to reiterate wha...</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>This is not a course on reading stories and s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>One of the first things that we shall be doin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Traditionally as the term implies you are awa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>We look at one particular genre, that is fict...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>So we will be unpacking this category and try...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>We will be approaching this course from three...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>We should first be looking at the historical ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>We will be looking at the critical tradition, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>We shall also be looking at the original text...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>We shall be focusing on to other major phases ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Overall this is an attempt to cover a vast li...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>And we hall be ending the discussion with rec...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>So this vast literary ground will not covered...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>On the contrary, we shall be looking a partic...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>We shall be looking at a range of things from ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>We shall be looking of the major frameworks o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Our pespectives will be enabled by the ideas ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>We will be looking at how the literary market...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>We shall also be looking at inherent elitism ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>I hope this course, Indian Fiction in English...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>If you're a student of English or if you are ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>I wish you a really fruitful learning experie...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Thank you so much for your attention and Than...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             sentence\n",
       "0                                      Hello everyone\n",
       "1    Thank you for your interest in this NPTEL cou...\n",
       "2    And Dr Merin Simi Raj, I teach in the departm...\n",
       "3    To give you a brief overview of this course t...\n",
       "4    This has been pitched at the post graduate level\n",
       "5    I must also tell you that we have been offeri...\n",
       "6    I would have ever look forward to for a lot o...\n",
       "7    Apart from that, I also need to reiterate wha...\n",
       "8    This is not a course on reading stories and s...\n",
       "9    One of the first things that we shall be doin...\n",
       "10   Traditionally as the term implies you are awa...\n",
       "11   We look at one particular genre, that is fict...\n",
       "12   So we will be unpacking this category and try...\n",
       "13   We will be approaching this course from three...\n",
       "14   We should first be looking at the historical ...\n",
       "15  We will be looking at the critical tradition, ...\n",
       "16   We shall also be looking at the original text...\n",
       "17  We shall be focusing on to other major phases ...\n",
       "18   Overall this is an attempt to cover a vast li...\n",
       "19   And we hall be ending the discussion with rec...\n",
       "20   So this vast literary ground will not covered...\n",
       "21   On the contrary, we shall be looking a partic...\n",
       "22  We shall be looking at a range of things from ...\n",
       "23   We shall be looking of the major frameworks o...\n",
       "24   Our pespectives will be enabled by the ideas ...\n",
       "25   We will be looking at how the literary market...\n",
       "26   We shall also be looking at inherent elitism ...\n",
       "27   I hope this course, Indian Fiction in English...\n",
       "28   If you're a student of English or if you are ...\n",
       "29   I wish you a really fruitful learning experie...\n",
       "30   Thank you so much for your attention and Than..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "textOP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28     If you're a student of English or if you are ...\n",
       "9      One of the first things that we shall be doin...\n",
       "2      And Dr Merin Simi Raj, I teach in the departm...\n",
       "5      I must also tell you that we have been offeri...\n",
       "17    We shall be focusing on to other major phases ...\n",
       "Name: sentence, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "textOP['sentence'].sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp=spacy.load('en_core_web_sm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc = nlp(textOP['sentence'][14])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  ... \n",
      "We ... nsubj\n",
      "should ... aux\n",
      "first ... advmod\n",
      "be ... aux\n",
      "looking ... ROOT\n",
      "at ... prep\n",
      "the ... det\n",
      "historical ... amod\n",
      "context ... pobj\n",
      "and ... cc\n",
      "looking ... conj\n",
      "at ... prep\n",
      "the ... det\n",
      "certain ... amod\n",
      "canonical ... amod\n",
      "text ... pobj\n",
      "which ... nsubj\n",
      "of ... advmod\n",
      "laid ... relcl\n",
      "the ... det\n",
      "foundations ... dobj\n",
      "of ... prep\n",
      "the ... det\n",
      "chronological ... amod\n",
      "and ... cc\n",
      "historical ... conj\n",
      "origins ... pobj\n",
      "of ... prep\n",
      "this ... det\n",
      "genre ... pobj\n"
     ]
    }
   ],
   "source": [
    "for tok in doc:\n",
    "    print(tok.text, \"...\", tok.dep_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from spacy.matcher import Matcher \n",
    "from spacy.tokens import Span \n",
    "from spacy import displacy \n",
    "\n",
    "# pd.set_option('display.max_colwidth', 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2150\">the</tspan>\n",
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       "\n",
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       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2675\">and</tspan>\n",
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       "\n",
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       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3375\">that</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3375\">DET</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3550\">are</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3550\">VERB</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3725\">available</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3725\">ADJ</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3900\">when</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3900\">ADV</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4075\">terms</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4075\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4250\">of</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4250\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4425\">authorship</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4425\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4600\">study</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4600\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4775\">and</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4775\">CCONJ</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4950\">in</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4950\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"5125\">terms</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"5125\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"5300\">of</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"5300\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"5475\">looking</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"5475\">VERB</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"5650\">at</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"5650\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"5825\">different</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"5825\">ADJ</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"6000\">kinds</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"6000\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"6175\">of</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"6175\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"6350\">genres</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"6350\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"6525\">and</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"6525\">CCONJ</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"6700\">also</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"6700\">ADV</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"6875\">talking</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"6875\">VERB</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"7050\">about</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"7050\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"7225\">issues</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"7225\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"7400\">related</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"7400\">VERB</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"7575\">to</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"7575\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"7750\">language</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"7750\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"7925\">and</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"7925\">CCONJ</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"8100\">identity</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"8100\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "\n",
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       "\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "    <path class=\"displacy-arrowhead\" d=\"M1425.0,704.0 L1433.0,692.0 1417.0,692.0\" fill=\"currentColor\"/>\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-448d0a9387d04f4fbf4219dae241f974-0-9\" stroke-width=\"2px\" d=\"M1820,702.0 C1820,614.5 1940.0,614.5 1940.0,702.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
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       "\n",
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       "    </text>\n",
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       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "' So we will be unpacking this category and try to interrogate the various contestations and the various definitions that are available when terms of authorship study and in terms of looking at different kinds of genres and also talking about issues related to language and identity'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text=textOP.iloc[12][0]\n",
    "doc = nlp(text) \n",
    "displacy.render(doc, style='dep',jupyter=True)\n",
    "text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "So\n",
      "we\n",
      "will\n",
      "be\n",
      "unpacking\n",
      "this\n",
      "category\n",
      "and\n",
      "try\n",
      "to\n",
      "interrogate\n",
      "the\n",
      "various\n",
      "contestations\n",
      "and\n",
      "the\n",
      "various\n",
      "definitions\n",
      "that\n",
      "are\n",
      "available\n",
      "when\n",
      "terms\n",
      "of\n",
      "authorship\n",
      "study\n",
      "and\n",
      "in\n",
      "terms\n",
      "of\n",
      "looking\n",
      "at\n",
      "different\n",
      "kinds\n",
      "of\n",
      "genres\n",
      "and\n",
      "also\n",
      "talking\n",
      "about\n",
      "issues\n",
      "related\n",
      "to\n",
      "language\n",
      "and\n",
      "identity\n"
     ]
    }
   ],
   "source": [
    "for tok in doc:\n",
    "    print(tok.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in range(len(textOP['sentence'])):\n",
    "#     doc = nlp(textOP['sentence'][i])\n",
    "#     for tok in doc:\n",
    "#         print(tok.text, \"...\", tok.dep_,\"...\",tok.pos_)\n",
    "#     print(\"sentance : \",i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_entities(sent):\n",
    "    ent1 = \"\"\n",
    "    ent2 = \"\"\n",
    "\n",
    "    prv_tok_dep = \"\"\n",
    "    prv_tok_text = \"\"\n",
    "    prefix = \"\"\n",
    "    modifier = \"\"\n",
    "\n",
    "    for tok in nlp(sent):\n",
    "        if tok.dep_ != \"punct\":\n",
    "            if tok.dep_ == \"compound\":\n",
    "                prefix = tok.text\n",
    "                if prv_tok_dep == \"compound\":\n",
    "                    prefix = prv_tok_text + \" \"+ tok.text\n",
    "        if tok.dep_.endswith(\"mod\") == True:\n",
    "            modifier = tok.text\n",
    "        if prv_tok_dep == \"compound\":\n",
    "            modifier = prv_tok_text + \" \"+ tok.text\n",
    "\n",
    "        if tok.dep_.find(\"subj\") == True:\n",
    "            ent1 = modifier +\" \"+ prefix + \" \"+ tok.text\n",
    "            prefix = \"\"\n",
    "            modifier = \"\"\n",
    "            prv_tok_dep = \"\"\n",
    "            prv_tok_text = \"\"      \n",
    "        if tok.dep_.find(\"obj\") == True:\n",
    "            ent2 = modifier +\" \"+ prefix +\" \"+ tok.text\n",
    "        prv_tok_dep = tok.dep_\n",
    "        prv_tok_text = tok.text\n",
    "#         print(\"{},{},{},{},{},{}\".format(ent1,ent2,prv_tok_dep,prv_tok_text,prefix,modifier))\n",
    "    return [ent1.strip(), ent2.strip()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['various  that', 'also authorship language']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_entities(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['film', '200  patents']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_entities(\"the film had 200 patents\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['film', 'known  patents']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_entities(\"the film had 200 well-known patents\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sheldon Cooper Sheldon Cooper', '3  PhDs']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_entities(\"Sheldon Cooper has 3 PhDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 31/31 [00:00<00:00, 39.95it/s]\n"
     ]
    }
   ],
   "source": [
    "entity_pairs = []\n",
    "\n",
    "for i in tqdm(textOP[\"sentence\"]):\n",
    "    entity_pairs.append(get_entities(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['', ''],\n",
       " ['', 'Indian Fiction Indian English'],\n",
       " ['Simi Raj Merin Simi I', 'IIT Madras IIT Madras'],\n",
       " ['12-week  that', '30  teaching'],\n",
       " ['This', 'graduate level post graduate level'],\n",
       " ['also  who', 'research Acumen research this'],\n",
       " ['forward  lot', 'side'],\n",
       " ['here  we', 'particular  text'],\n",
       " ['other  we', 'Indian Fiction study Indian English'],\n",
       " ['first  we', 'Indian Fiction Indian English'],\n",
       " ['who', 'Indian  origin'],\n",
       " ['English  we', 'other  English'],\n",
       " ['various  that', 'also authorship language'],\n",
       " ['We', 'major  angles'],\n",
       " ['canonical  which', 'chronological  genre'],\n",
       " ['critical  which', 'Meenakshi Mukherjee Meenakshi Mukherjee'],\n",
       " ['We', '19th fiction century'],\n",
       " ['We', 'major  post'],\n",
       " ['recent  we', 'Chandra Chatterjee Bankim Chandra Chatterjee'],\n",
       " ['hall', 'Allan Sealy Allan Zelaldinus'],\n",
       " ['literary  ground', 'summaries'],\n",
       " ['far  discipline', 'critical  tradition'],\n",
       " ['how  histroy', 'nationalist  histrography'],\n",
       " ['20th  which', 'such  colonialism'],\n",
       " ['pespectives', 'gender'],\n",
       " ['literary  market', 'genre'],\n",
       " ['inherent  which', 'well  English'],\n",
       " ['Indian Fiction Indian Fiction', 'much  you'],\n",
       " [\"Indian  you're\", 'course'],\n",
       " ['forward  you', 'course'],\n",
       " ['', 'much  video']]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entity_pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import pandas as pd\n",
    "import bs4\n",
    "import requests\n",
    "# import spacy\n",
    "from spacy import displacy\n",
    "\n",
    "from spacy.matcher import Matcher \n",
    "from spacy.tokens import Span \n",
    "\n",
    "import networkx as nx\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pd.set_option('display.max_colwidth', 200)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_relation(sent):\n",
    "    doc = nlp(sent)\n",
    "    matcher = Matcher(nlp.vocab)\n",
    "    pattern = [{'DEP':'ROOT'}, \n",
    "            {'DEP':'prep','OP':\"?\"},\n",
    "            {'DEP':'agent','OP':\"?\"},  \n",
    "            {'POS':'ADJ','OP':\"?\"}] \n",
    "\n",
    "    matcher.add(\"matching_1\", None, pattern) \n",
    "    matches = matcher(doc)\n",
    "    k = len(matches) - 1\n",
    "    span = doc[matches[k][1]:matches[k][2]] \n",
    "    return(span.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'completed'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_relation(\"John completed the task\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' This has been pitched at the post graduate level'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "textOP['sentence'][4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pitched at'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_relation(textOP['sentence'][4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 31/31 [00:00<00:00, 51.97it/s]\n"
     ]
    }
   ],
   "source": [
    "relations = [get_relation(i) for i in tqdm(textOP['sentence'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is                     4\n",
       "looking at             4\n",
       "Thank                  2\n",
       "unpacking              1\n",
       "teach in               1\n",
       "approaching            1\n",
       "work                   1\n",
       "Hello                  1\n",
       "covered through        1\n",
       "focusing on            1\n",
       "looking of             1\n",
       "looking at inherent    1\n",
       "looking                1\n",
       "pitched at             1\n",
       "implies                1\n",
       "wish                   1\n",
       "ending                 1\n",
       "look                   1\n",
       "be of much             1\n",
       "looking at various     1\n",
       "enabled by             1\n",
       "need                   1\n",
       "'re                    1\n",
       "are                    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(relations).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "source = [i[0] for i in entity_pairs]\n",
    "\n",
    "# extract object\n",
    "target = [i[1] for i in entity_pairs]\n",
    "\n",
    "kg_df = pd.DataFrame({'source':source, 'target':target, 'edge':relations})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "G=nx.from_pandas_edgelist(kg_df, \"source\", \"target\", \n",
    "                          edge_attr=True, create_using=nx.MultiDiGraph())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,12))\n",
    "\n",
    "pos = nx.spring_layout(G)\n",
    "nx.draw(G, with_labels=True, node_color='skyblue', edge_cmap=plt.cm.Blues, pos = pos)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}