Name Entity Recognition.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4bb77170",
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy\n",
    "nlp=spacy.load(\"en_core_web_sm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dd1a12f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tok2vec', 'tagger', 'parser', 'attribute_ruler', 'lemmatizer', 'ner']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlp.pipe_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "87262d39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tesla INC | ORG | Companies, agencies, institutions, etc.\n",
      "Twitter Inc | ORG | Companies, agencies, institutions, etc.\n",
      "$45 billion | MONEY | Monetary values, including unit\n"
     ]
    }
   ],
   "source": [
    "doc1=nlp(\"Tesla INC is going to acquire Twitter Inc for $45 billion\")\n",
    "for ent in doc1.ents:\n",
    "    print(ent.text,\"|\",ent.label_,\"|\",spacy.explain(ent.label_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "52e96961",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">\n",
       "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Tesla INC\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
       "</mark>\n",
       " is going to acquire \n",
       "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Twitter Inc\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
       "</mark>\n",
       " for \n",
       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    $45 billion\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
       "</mark>\n",
       "</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from spacy import displacy\n",
    "displacy.render(doc,style=\"ent\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4dee5fc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CARDINAL',\n",
       " 'DATE',\n",
       " 'EVENT',\n",
       " 'FAC',\n",
       " 'GPE',\n",
       " 'LANGUAGE',\n",
       " 'LAW',\n",
       " 'LOC',\n",
       " 'MONEY',\n",
       " 'NORP',\n",
       " 'ORDINAL',\n",
       " 'ORG',\n",
       " 'PERCENT',\n",
       " 'PERSON',\n",
       " 'PRODUCT',\n",
       " 'QUANTITY',\n",
       " 'TIME',\n",
       " 'WORK_OF_ART']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlp.pipe_labels['ner']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "176b89d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Michael Bloomberg | PERSON | People, including fictional\n",
      "Bloomberg L.P | PERSON | People, including fictional\n",
      "1982 | DATE | Absolute or relative dates or periods\n"
     ]
    }
   ],
   "source": [
    "doc2=nlp(\"Michael Bloomberg founded Bloomberg L.P in 1982\")\n",
    "for ent in doc2.ents:\n",
    "    print(ent.text,\"|\",ent.label_,\"|\",spacy.explain(ent.label_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cd24b90a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tesla | ORG\n",
      "Twitter | PRODUCT\n",
      "$45 billion | MONEY\n"
     ]
    }
   ],
   "source": [
    "doc3=nlp(\"Tesla is going to acquire Twitter for $45 billion\")\n",
    "for ent in doc3.ents:\n",
    "    print(ent.text,\"|\",ent.label_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4087e61e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tesla"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc3[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3633f8c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "spacy.tokens.token.Token"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(doc3[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4d5b99a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "going to acquire"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc3[2:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b6429305",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "spacy.tokens.span.Span"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(doc3[2:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4ec94c93",
   "metadata": {},
   "outputs": [],
   "source": [
    "from spacy.tokens import Span"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "dbdf6e68",
   "metadata": {},
   "outputs": [],
   "source": [
    "s1=Span(doc3,0,1,label=\"ORG\")\n",
    "s2=Span(doc3,5,6,label=\"ORG\")\n",
    "doc3.set_ents([s1,s2],default=\"unmodified\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "8668b491",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tesla | ORG\n",
      "Twitter | ORG\n",
      "$45 billion | MONEY\n"
     ]
    }
   ],
   "source": [
    "for ent in doc3.ents:\n",
    "    print(ent.text,\"|\",ent.label_)"
   ]
  }
 ],
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