Lab 4/LAB-4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1ff0dce6",
   "metadata": {},
   "source": [
    "# POS TAGGING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fcd173b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package treebank to\n",
      "[nltk_data]     C:\\Users\\ganes\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package treebank is already up-to-date!\n",
      "[nltk_data] Downloading package universal_tagset to\n",
      "[nltk_data]     C:\\Users\\ganes\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package universal_tagset is already up-to-date!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[('Pierre', 'NOUN'), ('Vinken', 'NOUN'), (',', '.'), ('61', 'NUM'), ('years', 'NOUN'), ('old', 'ADJ'), (',', '.'), ('will', 'VERB'), ('join', 'VERB'), ('the', 'DET'), ('board', 'NOUN'), ('as', 'ADP'), ('a', 'DET'), ('nonexecutive', 'ADJ'), ('director', 'NOUN'), ('Nov.', 'NOUN'), ('29', 'NUM'), ('.', '.')]]\n"
     ]
    }
   ],
   "source": [
    "# Importing libraries\n",
    "import nltk\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pprint, time\n",
    " \n",
    "#download the treebank corpus from nltk\n",
    "nltk.download('treebank')\n",
    " \n",
    "#download the universal tagset from nltk\n",
    "nltk.download('universal_tagset')\n",
    " \n",
    "# reading the Treebank tagged sentences\n",
    "nltk_data = list(nltk.corpus.treebank.tagged_sents(tagset='universal'))\n",
    " \n",
    "#print the first two sentences along with tags\n",
    "print(nltk_data[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5fbd3bdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Pierre', 'NOUN')\n",
      "('Vinken', 'NOUN')\n",
      "(',', '.')\n",
      "('61', 'NUM')\n",
      "('years', 'NOUN')\n",
      "('old', 'ADJ')\n",
      "(',', '.')\n",
      "('will', 'VERB')\n",
      "('join', 'VERB')\n",
      "('the', 'DET')\n",
      "('board', 'NOUN')\n",
      "('as', 'ADP')\n",
      "('a', 'DET')\n",
      "('nonexecutive', 'ADJ')\n",
      "('director', 'NOUN')\n",
      "('Nov.', 'NOUN')\n",
      "('29', 'NUM')\n",
      "('.', '.')\n",
      "('Mr.', 'NOUN')\n",
      "('Vinken', 'NOUN')\n",
      "('is', 'VERB')\n",
      "('chairman', 'NOUN')\n",
      "('of', 'ADP')\n",
      "('Elsevier', 'NOUN')\n",
      "('N.V.', 'NOUN')\n",
      "(',', '.')\n",
      "('the', 'DET')\n",
      "('Dutch', 'NOUN')\n",
      "('publishing', 'VERB')\n",
      "('group', 'NOUN')\n",
      "('.', '.')\n"
     ]
    }
   ],
   "source": [
    "#print each word with its respective tag for first two sentences\n",
    "for sent in nltk_data[:2]:\n",
    "    for tuple in sent:\n",
    "        print(tuple)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bda7d9e6",
   "metadata": {},
   "source": [
    "# CHUNKING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bd0b8d6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the required libraries\n",
    "import nltk\n",
    "from nltk import pos_tag\n",
    "from nltk import word_tokenize\n",
    "from nltk import RegexpParser\n",
    "\n",
    "# Example sentence\n",
    "text = \" The Air India flight to Delhi was ready to board.\"\n",
    "\n",
    "# Splitiing the sentence into words\n",
    "list_of_words = word_tokenize(text)\n",
    "\n",
    "# Applying POS_tagging\n",
    "tagged_words = pos_tag(list_of_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9761e55c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting the Noun Phrases\n",
    "chunk_to_be_extracted = r''' Chunk: {<DT>*<NNP>*<NN>*} '''\n",
    "\n",
    "# Applying chunking to the text\n",
    "chunkParser = nltk.chunk.RegexpParser(chunk_to_be_extracted)\n",
    "chunked_sentence = chunkParser.parse(tagged_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d9d5f4c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To view the NLTK tree\n",
    "chunked_sentence.draw() "
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "123629d3",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9b150dfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chunks obtained: \n",
      "\n",
      "(Chunk The/DT Air/NNP India/NNP flight/NN)\n",
      "(Chunk Delhi/NNP)\n",
      "(Chunk board/NN)\n"
     ]
    }
   ],
   "source": [
    "# To print the chunks extracted\n",
    "\n",
    "print('Chunks obtained: \\n')\n",
    "for subtree in chunked_sentence.subtrees():\n",
    "    if subtree.label() == 'Chunk':\n",
    "        print(subtree)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7e08af3",
   "metadata": {},
   "source": [
    "# Named Entity Recognition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e4103d8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package maxent_ne_chunker to\n",
      "[nltk_data]     C:\\Users\\vivek\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Unzipping chunkers\\maxent_ne_chunker.zip.\n"
     ]
    },
    {
     "ename": "LookupError",
     "evalue": "\n**********************************************************************\n  Resource \u001b[93mwords\u001b[0m not found.\n  Please use the NLTK Downloader to obtain the resource:\n\n  \u001b[31m>>> import nltk\n  >>> nltk.download('words')\n  \u001b[0m\n  For more information see: https://www.nltk.org/data.html\n\n  Attempted to load \u001b[93mcorpora/words\u001b[0m\n\n  Searched in:\n    - 'C:\\\\Users\\\\vivek/nltk_data'\n    - 'C:\\\\Users\\\\vivek\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\nltk_data'\n    - 'C:\\\\Users\\\\vivek\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\share\\\\nltk_data'\n    - 'C:\\\\Users\\\\vivek\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\lib\\\\nltk_data'\n    - 'C:\\\\Users\\\\vivek\\\\AppData\\\\Roaming\\\\nltk_data'\n    - 'C:\\\\nltk_data'\n    - 'D:\\\\nltk_data'\n    - 'E:\\\\nltk_data'\n**********************************************************************\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mLookupError\u001b[0m                               Traceback (most recent call last)",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\nltk\\corpus\\util.py:84\u001b[0m, in \u001b[0;36mLazyCorpusLoader.__load\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     83\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 84\u001b[0m     root \u001b[38;5;241m=\u001b[39m \u001b[43mnltk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfind\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubdir\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m/\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mzip_name\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m     85\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mLookupError\u001b[39;00m:\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\nltk\\data.py:583\u001b[0m, in \u001b[0;36mfind\u001b[1;34m(resource_name, paths)\u001b[0m\n\u001b[0;32m    582\u001b[0m resource_not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00msep\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mmsg\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00msep\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m--> 583\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mLookupError\u001b[39;00m(resource_not_found)\n",
      "\u001b[1;31mLookupError\u001b[0m: \n**********************************************************************\n  Resource \u001b[93mwords\u001b[0m not found.\n  Please use the NLTK Downloader to obtain the resource:\n\n  \u001b[31m>>> import nltk\n  >>> nltk.download('words')\n  \u001b[0m\n  For more information see: https://www.nltk.org/data.html\n\n  Attempted to load \u001b[93mcorpora/words.zip/words/\u001b[0m\n\n  Searched in:\n    - 'C:\\\\Users\\\\vivek/nltk_data'\n    - 'C:\\\\Users\\\\vivek\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\nltk_data'\n    - 'C:\\\\Users\\\\vivek\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\share\\\\nltk_data'\n    - 'C:\\\\Users\\\\vivek\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\lib\\\\nltk_data'\n    - 'C:\\\\Users\\\\vivek\\\\AppData\\\\Roaming\\\\nltk_data'\n    - 'C:\\\\nltk_data'\n    - 'D:\\\\nltk_data'\n    - 'E:\\\\nltk_data'\n**********************************************************************\n",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mLookupError\u001b[0m                               Traceback (most recent call last)",
      "Input \u001b[1;32mIn [2]\u001b[0m, in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m words \u001b[38;5;241m=\u001b[39m word_tokenize(text) \u001b[38;5;66;03m# Tokenize the text\u001b[39;00m\n\u001b[0;32m      7\u001b[0m words_pos \u001b[38;5;241m=\u001b[39m pos_tag(words) \u001b[38;5;66;03m# Part-of-speech (POS) tagging\u001b[39;00m\n\u001b[1;32m----> 8\u001b[0m entities \u001b[38;5;241m=\u001b[39m \u001b[43mne_chunk\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwords_pos\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# Named Entity Recognition\u001b[39;00m\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m entity \u001b[38;5;129;01min\u001b[39;00m entities\u001b[38;5;241m.\u001b[39msubtrees():\n\u001b[0;32m     10\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m entity\u001b[38;5;241m.\u001b[39mlabel() \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mS\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\nltk\\chunk\\__init__.py:184\u001b[0m, in \u001b[0;36mne_chunk\u001b[1;34m(tagged_tokens, binary)\u001b[0m\n\u001b[0;32m    182\u001b[0m     chunker_pickle \u001b[38;5;241m=\u001b[39m _MULTICLASS_NE_CHUNKER\n\u001b[0;32m    183\u001b[0m chunker \u001b[38;5;241m=\u001b[39m load(chunker_pickle)\n\u001b[1;32m--> 184\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mchunker\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtagged_tokens\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\nltk\\chunk\\named_entity.py:127\u001b[0m, in \u001b[0;36mNEChunkParser.parse\u001b[1;34m(self, tokens)\u001b[0m\n\u001b[0;32m    123\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mparse\u001b[39m(\u001b[38;5;28mself\u001b[39m, tokens):\n\u001b[0;32m    124\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    125\u001b[0m \u001b[38;5;124;03m    Each token should be a pos-tagged word\u001b[39;00m\n\u001b[0;32m    126\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 127\u001b[0m     tagged \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_tagger\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtag\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtokens\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    128\u001b[0m     tree \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tagged_to_parse(tagged)\n\u001b[0;32m    129\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m tree\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\nltk\\tag\\sequential.py:61\u001b[0m, in \u001b[0;36mSequentialBackoffTagger.tag\u001b[1;34m(self, tokens)\u001b[0m\n\u001b[0;32m     59\u001b[0m tags \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m     60\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(tokens)):\n\u001b[1;32m---> 61\u001b[0m     tags\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtag_one\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtokens\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtags\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m     62\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mzip\u001b[39m(tokens, tags))\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\nltk\\tag\\sequential.py:81\u001b[0m, in \u001b[0;36mSequentialBackoffTagger.tag_one\u001b[1;34m(self, tokens, index, history)\u001b[0m\n\u001b[0;32m     79\u001b[0m tag \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m     80\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m tagger \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_taggers:\n\u001b[1;32m---> 81\u001b[0m     tag \u001b[38;5;241m=\u001b[39m \u001b[43mtagger\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchoose_tag\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtokens\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhistory\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     82\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m tag \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m     83\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\nltk\\tag\\sequential.py:647\u001b[0m, in \u001b[0;36mClassifierBasedTagger.choose_tag\u001b[1;34m(self, tokens, index, history)\u001b[0m\n\u001b[0;32m    645\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mchoose_tag\u001b[39m(\u001b[38;5;28mself\u001b[39m, tokens, index, history):\n\u001b[0;32m    646\u001b[0m     \u001b[38;5;66;03m# Use our feature detector to get the featureset.\u001b[39;00m\n\u001b[1;32m--> 647\u001b[0m     featureset \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeature_detector\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtokens\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhistory\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    649\u001b[0m     \u001b[38;5;66;03m# Use the classifier to pick a tag.  If a cutoff probability\u001b[39;00m\n\u001b[0;32m    650\u001b[0m     \u001b[38;5;66;03m# was specified, then check that the tag's probability is\u001b[39;00m\n\u001b[0;32m    651\u001b[0m     \u001b[38;5;66;03m# higher than that cutoff first; otherwise, return None.\u001b[39;00m\n\u001b[0;32m    652\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cutoff_prob \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\nltk\\tag\\sequential.py:694\u001b[0m, in \u001b[0;36mClassifierBasedTagger.feature_detector\u001b[1;34m(self, tokens, index, history)\u001b[0m\n\u001b[0;32m    684\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfeature_detector\u001b[39m(\u001b[38;5;28mself\u001b[39m, tokens, index, history):\n\u001b[0;32m    685\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    686\u001b[0m \u001b[38;5;124;03m    Return the feature detector that this tagger uses to generate\u001b[39;00m\n\u001b[0;32m    687\u001b[0m \u001b[38;5;124;03m    featuresets for its classifier.  The feature detector is a\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    692\u001b[0m \u001b[38;5;124;03m    See ``classifier()``\u001b[39;00m\n\u001b[0;32m    693\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 694\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_feature_detector\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtokens\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhistory\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\nltk\\chunk\\named_entity.py:101\u001b[0m, in \u001b[0;36mNEChunkParserTagger._feature_detector\u001b[1;34m(self, tokens, index, history)\u001b[0m\n\u001b[0;32m     90\u001b[0m     nextnextpos \u001b[38;5;241m=\u001b[39m tokens[index \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m2\u001b[39m][\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mlower()\n\u001b[0;32m     92\u001b[0m \u001b[38;5;66;03m# 89.6\u001b[39;00m\n\u001b[0;32m     93\u001b[0m features \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m     94\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbias\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m     95\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshape\u001b[39m\u001b[38;5;124m\"\u001b[39m: shape(word),\n\u001b[0;32m     96\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwordlen\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mlen\u001b[39m(word),\n\u001b[0;32m     97\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprefix3\u001b[39m\u001b[38;5;124m\"\u001b[39m: word[:\u001b[38;5;241m3\u001b[39m]\u001b[38;5;241m.\u001b[39mlower(),\n\u001b[0;32m     98\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msuffix3\u001b[39m\u001b[38;5;124m\"\u001b[39m: word[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m3\u001b[39m:]\u001b[38;5;241m.\u001b[39mlower(),\n\u001b[0;32m     99\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpos\u001b[39m\u001b[38;5;124m\"\u001b[39m: pos,\n\u001b[0;32m    100\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mword\u001b[39m\u001b[38;5;124m\"\u001b[39m: word,\n\u001b[1;32m--> 101\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124men-wordlist\u001b[39m\u001b[38;5;124m\"\u001b[39m: (word \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_english_wordlist\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m),\n\u001b[0;32m    102\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprevtag\u001b[39m\u001b[38;5;124m\"\u001b[39m: prevtag,\n\u001b[0;32m    103\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprevpos\u001b[39m\u001b[38;5;124m\"\u001b[39m: prevpos,\n\u001b[0;32m    104\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnextpos\u001b[39m\u001b[38;5;124m\"\u001b[39m: nextpos,\n\u001b[0;32m    105\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprevword\u001b[39m\u001b[38;5;124m\"\u001b[39m: prevword,\n\u001b[0;32m    106\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnextword\u001b[39m\u001b[38;5;124m\"\u001b[39m: nextword,\n\u001b[0;32m    107\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mword+nextpos\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mword\u001b[38;5;241m.\u001b[39mlower()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m+\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnextpos\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    108\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpos+prevtag\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpos\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m+\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mprevtag\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    109\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshape+prevtag\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mprevshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m+\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mprevtag\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    110\u001b[0m }\n\u001b[0;32m    112\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m features\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\nltk\\chunk\\named_entity.py:52\u001b[0m, in \u001b[0;36mNEChunkParserTagger._english_wordlist\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     49\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[0;32m     50\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnltk\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcorpus\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m words\n\u001b[1;32m---> 52\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_en_wordlist \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m(\u001b[43mwords\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwords\u001b[49m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124men-basic\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m     53\u001b[0m     wl \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_en_wordlist\n\u001b[0;32m     54\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m wl\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\nltk\\corpus\\util.py:121\u001b[0m, in \u001b[0;36mLazyCorpusLoader.__getattr__\u001b[1;34m(self, attr)\u001b[0m\n\u001b[0;32m    118\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m attr \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__bases__\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m    119\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLazyCorpusLoader object has no attribute \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__bases__\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 121\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__load\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    122\u001b[0m \u001b[38;5;66;03m# This looks circular, but its not, since __load() changes our\u001b[39;00m\n\u001b[0;32m    123\u001b[0m \u001b[38;5;66;03m# __class__ to something new:\u001b[39;00m\n\u001b[0;32m    124\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, attr)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\nltk\\corpus\\util.py:86\u001b[0m, in \u001b[0;36mLazyCorpusLoader.__load\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     84\u001b[0m             root \u001b[38;5;241m=\u001b[39m nltk\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mfind(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubdir\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mzip_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     85\u001b[0m         \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mLookupError\u001b[39;00m:\n\u001b[1;32m---> 86\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[0;32m     88\u001b[0m \u001b[38;5;66;03m# Load the corpus.\u001b[39;00m\n\u001b[0;32m     89\u001b[0m corpus \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__reader_cls(root, \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__kwargs)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\nltk\\corpus\\util.py:81\u001b[0m, in \u001b[0;36mLazyCorpusLoader.__load\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     79\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     80\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 81\u001b[0m         root \u001b[38;5;241m=\u001b[39m \u001b[43mnltk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfind\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubdir\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m/\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__name\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m     82\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mLookupError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     83\u001b[0m         \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\nltk\\data.py:583\u001b[0m, in \u001b[0;36mfind\u001b[1;34m(resource_name, paths)\u001b[0m\n\u001b[0;32m    581\u001b[0m sep \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m*\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m70\u001b[39m\n\u001b[0;32m    582\u001b[0m resource_not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00msep\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mmsg\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00msep\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m--> 583\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mLookupError\u001b[39;00m(resource_not_found)\n",
      "\u001b[1;31mLookupError\u001b[0m: \n**********************************************************************\n  Resource \u001b[93mwords\u001b[0m not found.\n  Please use the NLTK Downloader to obtain the resource:\n\n  \u001b[31m>>> import nltk\n  >>> nltk.download('words')\n  \u001b[0m\n  For more information see: https://www.nltk.org/data.html\n\n  Attempted to load \u001b[93mcorpora/words\u001b[0m\n\n  Searched in:\n    - 'C:\\\\Users\\\\vivek/nltk_data'\n    - 'C:\\\\Users\\\\vivek\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\nltk_data'\n    - 'C:\\\\Users\\\\vivek\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\share\\\\nltk_data'\n    - 'C:\\\\Users\\\\vivek\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\lib\\\\nltk_data'\n    - 'C:\\\\Users\\\\vivek\\\\AppData\\\\Roaming\\\\nltk_data'\n    - 'C:\\\\nltk_data'\n    - 'D:\\\\nltk_data'\n    - 'E:\\\\nltk_data'\n**********************************************************************\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk import word_tokenize, pos_tag, ne_chunk\n",
    "nltk.download('maxent_ne_chunker')\n",
    "\n",
    "text = \"Apple is looking at buying U.K. startup for $1 billion\"\n",
    "words = word_tokenize(text) # Tokenize the text\n",
    "words_pos = pos_tag(words) # Part-of-speech (POS) tagging\n",
    "entities = ne_chunk(words_pos) # Named Entity Recognition\n",
    "for entity in entities.subtrees():\n",
    "    if entity.label() != 'S':\n",
    "        print(entity.label(), \" \".join(word for word, pos in entity.leaves()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "07b1a345",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.tag import pos_tag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "de1ce7bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "ex = \"\"\"European authorities fined Google a record $5.1 billion on Wednesday \n",
    "        for abusing its power in the mobile phone market and ordered the company to alter its practices\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0ea35d64",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(sent):\n",
    "    sent = nltk.word_tokenize(sent)\n",
    "    sent = nltk.pos_tag(sent)\n",
    "    return sent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1369601f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('European', 'JJ'),\n",
       " ('authorities', 'NNS'),\n",
       " ('fined', 'VBD'),\n",
       " ('Google', 'NNP'),\n",
       " ('a', 'DT'),\n",
       " ('record', 'NN'),\n",
       " ('$', '$'),\n",
       " ('5.1', 'CD'),\n",
       " ('billion', 'CD'),\n",
       " ('on', 'IN'),\n",
       " ('Wednesday', 'NNP'),\n",
       " ('for', 'IN'),\n",
       " ('abusing', 'VBG'),\n",
       " ('its', 'PRP$'),\n",
       " ('power', 'NN'),\n",
       " ('in', 'IN'),\n",
       " ('the', 'DT'),\n",
       " ('mobile', 'JJ'),\n",
       " ('phone', 'NN'),\n",
       " ('market', 'NN'),\n",
       " ('and', 'CC'),\n",
       " ('ordered', 'VBD'),\n",
       " ('the', 'DT'),\n",
       " ('company', 'NN'),\n",
       " ('to', 'TO'),\n",
       " ('alter', 'VB'),\n",
       " ('its', 'PRP$'),\n",
       " ('practices', 'NNS')]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent = preprocess(ex)\n",
    "sent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "14ac3b9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(S\n",
      "  European/JJ\n",
      "  authorities/NNS\n",
      "  fined/VBD\n",
      "  Google/NNP\n",
      "  (NP a/DT record/NN)\n",
      "  $/$\n",
      "  5.1/CD\n",
      "  billion/CD\n",
      "  on/IN\n",
      "  Wednesday/NNP\n",
      "  for/IN\n",
      "  abusing/VBG\n",
      "  its/PRP$\n",
      "  (NP power/NN)\n",
      "  in/IN\n",
      "  (NP the/DT mobile/JJ phone/NN)\n",
      "  (NP market/NN)\n",
      "  and/CC\n",
      "  ordered/VBD\n",
      "  (NP the/DT company/NN)\n",
      "  to/TO\n",
      "  alter/VB\n",
      "  its/PRP$\n",
      "  practices/NNS)\n"
     ]
    }
   ],
   "source": [
    "pattern = 'NP: {<DT>?<JJ>*<NN>}'\n",
    "cp = nltk.RegexpParser(pattern)\n",
    "cs = cp.parse(sent)\n",
    "print(cs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c44c9505",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('European', 'JJ', 'O'),\n",
      " ('authorities', 'NNS', 'O'),\n",
      " ('fined', 'VBD', 'O'),\n",
      " ('Google', 'NNP', 'O'),\n",
      " ('a', 'DT', 'B-NP'),\n",
      " ('record', 'NN', 'I-NP'),\n",
      " ('$', '$', 'O'),\n",
      " ('5.1', 'CD', 'O'),\n",
      " ('billion', 'CD', 'O'),\n",
      " ('on', 'IN', 'O'),\n",
      " ('Wednesday', 'NNP', 'O'),\n",
      " ('for', 'IN', 'O'),\n",
      " ('abusing', 'VBG', 'O'),\n",
      " ('its', 'PRP$', 'O'),\n",
      " ('power', 'NN', 'B-NP'),\n",
      " ('in', 'IN', 'O'),\n",
      " ('the', 'DT', 'B-NP'),\n",
      " ('mobile', 'JJ', 'I-NP'),\n",
      " ('phone', 'NN', 'I-NP'),\n",
      " ('market', 'NN', 'B-NP'),\n",
      " ('and', 'CC', 'O'),\n",
      " ('ordered', 'VBD', 'O'),\n",
      " ('the', 'DT', 'B-NP'),\n",
      " ('company', 'NN', 'I-NP'),\n",
      " ('to', 'TO', 'O'),\n",
      " ('alter', 'VB', 'O'),\n",
      " ('its', 'PRP$', 'O'),\n",
      " ('practices', 'NNS', 'O')]\n"
     ]
    }
   ],
   "source": [
    "from nltk.chunk import conlltags2tree, tree2conlltags\n",
    "from pprint import pprint\n",
    "iob_tagged = tree2conlltags(cs)\n",
    "pprint(iob_tagged)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1b051252",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(S\n",
      "  (GPE European/JJ)\n",
      "  authorities/NNS\n",
      "  fined/VBD\n",
      "  (PERSON Google/NNP)\n",
      "  a/DT\n",
      "  record/NN\n",
      "  $/$\n",
      "  5.1/CD\n",
      "  billion/CD\n",
      "  on/IN\n",
      "  Wednesday/NNP\n",
      "  for/IN\n",
      "  abusing/VBG\n",
      "  its/PRP$\n",
      "  power/NN\n",
      "  in/IN\n",
      "  the/DT\n",
      "  mobile/JJ\n",
      "  phone/NN\n",
      "  market/NN\n",
      "  and/CC\n",
      "  ordered/VBD\n",
      "  the/DT\n",
      "  company/NN\n",
      "  to/TO\n",
      "  alter/VB\n",
      "  its/PRP$\n",
      "  practices/NNS)\n"
     ]
    }
   ],
   "source": [
    "ne_tree = ne_chunk(pos_tag(word_tokenize(ex)))\n",
    "print(ne_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ce70a1d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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