8-Parts+Of+Speech+Tagging.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parts Of Speech Tags"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Taj Mahal is a beautiful Monument\""
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "CC coordinating conjunction \n",
    "CD cardinal digit \n",
    "DT determiner \n",
    "EX existential there (like: “there is” … think of it like “there exists”) \n",
    "FW foreign word \n",
    "IN preposition/subordinating conjunction \n",
    "JJ adjective – ‘big’ \n",
    "JJR adjective, comparative – ‘bigger’ \n",
    "JJS adjective, superlative – ‘biggest’ \n",
    "LS list marker 1) \n",
    "MD modal – could, will \n",
    "NN noun, singular ‘- desk’ \n",
    "NNS noun plural – ‘desks’ \n",
    "NNP proper noun, singular – ‘Harrison’ \n",
    "NNPS proper noun, plural – ‘Americans’ \n",
    "PDT predeterminer – ‘all the kids’ \n",
    "POS possessive ending parent’s \n",
    "PRP personal pronoun –  I, he, she \n",
    "PRP$ possessive pronoun – my, his, hers \n",
    "RB adverb – very, silently, \n",
    "RBR adverb, comparative – better \n",
    "RBS adverb, superlative – best \n",
    "RP particle – give up \n",
    "TO – to go ‘to’ the store. \n",
    "UH interjection – errrrrrrrm \n",
    "VB verb, base form – take \n",
    "VBD verb, past tense – took \n",
    "VBG verb, gerund/present participle – taking \n",
    "VBN verb, past participle – taken \n",
    "VBP verb, sing. present, non-3d – take \n",
    "VBZ verb, 3rd person sing. present – takes \n",
    "WDT wh-determiner – which \n",
    "WP wh-pronoun – who, what \n",
    "WP$ possessive wh-pronoun, eg- whose \n",
    "WRB wh-adverb, eg- where, when"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Speech Of DR APJ Abdul Kalam\n",
    "paragraph = \"\"\"I have three visions for India. In 3000 years of our history, people from all over \n",
    "               the world have come and invaded us, captured our lands, conquered our minds. \n",
    "               From Alexander onwards, the Greeks, the Turks, the Moguls, the Portuguese, the British,\n",
    "               the French, the Dutch, all of them came and looted us, took over what was ours. \n",
    "               Yet we have not done this to any other nation. We have not conquered anyone. \n",
    "               We have not grabbed their land, their culture, \n",
    "               their history and tried to enforce our way of life on them. \n",
    "               Why? Because we respect the freedom of others.That is why my \n",
    "               first vision is that of freedom. I believe that India got its first vision of \n",
    "               this in 1857, when we started the War of Independence. It is this freedom that\n",
    "               we must protect and nurture and build on. If we are not free, no one will respect us.\n",
    "               My second vision for India’s development. For fifty years we have been a developing nation.\n",
    "               It is time we see ourselves as a developed nation. We are among the top 5 nations of the world\n",
    "               in terms of GDP. We have a 10 percent growth rate in most areas. Our poverty levels are falling.\n",
    "               Our achievements are being globally recognised today. Yet we lack the self-confidence to\n",
    "               see ourselves as a developed nation, self-reliant and self-assured. Isn’t this incorrect?\n",
    "               I have a third vision. India must stand up to the world. Because I believe that unless India \n",
    "               stands up to the world, no one will respect us. Only strength respects strength. We must be \n",
    "               strong not only as a military power but also as an economic power. Both must go hand-in-hand. \n",
    "               My good fortune was to have worked with three great minds. Dr. Vikram Sarabhai of the Dept. of \n",
    "               space, Professor Satish Dhawan, who succeeded him and Dr. Brahm Prakash, father of nuclear material.\n",
    "               I was lucky to have worked with all three of them closely and consider this the great opportunity of my life. \n",
    "               I see four milestones in my career\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "sentences=nltk.sent_tokenize(paragraph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['I have three visions for India.',\n",
       " 'In 3000 years of our history, people from all over \\n               the world have come and invaded us, captured our lands, conquered our minds.',\n",
       " 'From Alexander onwards, the Greeks, the Turks, the Moguls, the Portuguese, the British,\\n               the French, the Dutch, all of them came and looted us, took over what was ours.',\n",
       " 'Yet we have not done this to any other nation.',\n",
       " 'We have not conquered anyone.',\n",
       " 'We have not grabbed their land, their culture, \\n               their history and tried to enforce our way of life on them.',\n",
       " 'Why?',\n",
       " 'Because we respect the freedom of others.That is why my \\n               first vision is that of freedom.',\n",
       " 'I believe that India got its first vision of \\n               this in 1857, when we started the War of Independence.',\n",
       " 'It is this freedom that\\n               we must protect and nurture and build on.',\n",
       " 'If we are not free, no one will respect us.',\n",
       " 'My second vision for India’s development.',\n",
       " 'For fifty years we have been a developing nation.',\n",
       " 'It is time we see ourselves as a developed nation.',\n",
       " 'We are among the top 5 nations of the world\\n               in terms of GDP.',\n",
       " 'We have a 10 percent growth rate in most areas.',\n",
       " 'Our poverty levels are falling.',\n",
       " 'Our achievements are being globally recognised today.',\n",
       " 'Yet we lack the self-confidence to\\n               see ourselves as a developed nation, self-reliant and self-assured.',\n",
       " 'Isn’t this incorrect?',\n",
       " 'I have a third vision.',\n",
       " 'India must stand up to the world.',\n",
       " 'Because I believe that unless India \\n               stands up to the world, no one will respect us.',\n",
       " 'Only strength respects strength.',\n",
       " 'We must be \\n               strong not only as a military power but also as an economic power.',\n",
       " 'Both must go hand-in-hand.',\n",
       " 'My good fortune was to have worked with three great minds.',\n",
       " 'Dr. Vikram Sarabhai of the Dept.',\n",
       " 'of \\n               space, Professor Satish Dhawan, who succeeded him and Dr. Brahm Prakash, father of nuclear material.',\n",
       " 'I was lucky to have worked with all three of them closely and consider this the great opportunity of my life.',\n",
       " 'I see four milestones in my career']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     C:\\Users\\nagar\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
      "[nltk_data]       date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('averaged_perceptron_tagger')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "LookupError",
     "evalue": "\n**********************************************************************\n  Resource \u001b[93maveraged_perceptron_tagger_eng\u001b[0m not found.\n  Please use the NLTK Downloader to obtain the resource:\n\n  \u001b[31m>>> import nltk\n  >>> nltk.download('averaged_perceptron_tagger_eng')\n  \u001b[0m\n  For more information see: https://www.nltk.org/data.html\n\n  Attempted to load \u001b[93mtaggers/averaged_perceptron_tagger_eng/\u001b[0m\n\n  Searched in:\n    - 'C:\\\\Users\\\\nagar/nltk_data'\n    - 'C:\\\\Program Files\\\\WindowsApps\\\\PythonSoftwareFoundation.Python.3.12_3.12.2288.0_x64__qbz5n2kfra8p0\\\\nltk_data'\n    - 'C:\\\\Program Files\\\\WindowsApps\\\\PythonSoftwareFoundation.Python.3.12_3.12.2288.0_x64__qbz5n2kfra8p0\\\\share\\\\nltk_data'\n    - 'C:\\\\Program Files\\\\WindowsApps\\\\PythonSoftwareFoundation.Python.3.12_3.12.2288.0_x64__qbz5n2kfra8p0\\\\lib\\\\nltk_data'\n    - 'C:\\\\Users\\\\nagar\\\\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)",
      "Cell \u001b[1;32mIn[8], line 7\u001b[0m\n\u001b[0;32m      5\u001b[0m words\u001b[38;5;241m=\u001b[39m[word \u001b[38;5;28;01mfor\u001b[39;00m word \u001b[38;5;129;01min\u001b[39;00m words \u001b[38;5;28;01mif\u001b[39;00m word \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mset\u001b[39m(stopwords\u001b[38;5;241m.\u001b[39mwords(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124menglish\u001b[39m\u001b[38;5;124m'\u001b[39m))]\n\u001b[0;32m      6\u001b[0m \u001b[38;5;66;03m#sentences[i]=' '.join(words)# converting all the list of words into sentences\u001b[39;00m\n\u001b[1;32m----> 7\u001b[0m pos_tag\u001b[38;5;241m=\u001b[39m\u001b[43mnltk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpos_tag\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwords\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28mprint\u001b[39m(pos_tag)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\nltk\\tag\\__init__.py:168\u001b[0m, in \u001b[0;36mpos_tag\u001b[1;34m(tokens, tagset, lang)\u001b[0m\n\u001b[0;32m    143\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpos_tag\u001b[39m(tokens, tagset\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, lang\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meng\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m    144\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    145\u001b[0m \u001b[38;5;124;03m    Use NLTK's currently recommended part of speech tagger to\u001b[39;00m\n\u001b[0;32m    146\u001b[0m \u001b[38;5;124;03m    tag the given list of tokens.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    166\u001b[0m \u001b[38;5;124;03m    :rtype: list(tuple(str, str))\u001b[39;00m\n\u001b[0;32m    167\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 168\u001b[0m     tagger \u001b[38;5;241m=\u001b[39m \u001b[43m_get_tagger\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlang\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    169\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _pos_tag(tokens, tagset, tagger, lang)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\nltk\\tag\\__init__.py:110\u001b[0m, in \u001b[0;36m_get_tagger\u001b[1;34m(lang)\u001b[0m\n\u001b[0;32m    108\u001b[0m     tagger \u001b[38;5;241m=\u001b[39m PerceptronTagger(lang\u001b[38;5;241m=\u001b[39mlang)\n\u001b[0;32m    109\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 110\u001b[0m     tagger \u001b[38;5;241m=\u001b[39m \u001b[43mPerceptronTagger\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    111\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m tagger\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\nltk\\tag\\perceptron.py:183\u001b[0m, in \u001b[0;36mPerceptronTagger.__init__\u001b[1;34m(self, load, lang)\u001b[0m\n\u001b[0;32m    181\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclasses \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n\u001b[0;32m    182\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m load:\n\u001b[1;32m--> 183\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_from_json\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlang\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\nltk\\tag\\perceptron.py:273\u001b[0m, in \u001b[0;36mPerceptronTagger.load_from_json\u001b[1;34m(self, lang)\u001b[0m\n\u001b[0;32m    271\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_from_json\u001b[39m(\u001b[38;5;28mself\u001b[39m, lang\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meng\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m    272\u001b[0m     \u001b[38;5;66;03m# Automatically find path to the tagger if location is not specified.\u001b[39;00m\n\u001b[1;32m--> 273\u001b[0m     loc \u001b[38;5;241m=\u001b[39m \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;124;43mtaggers/averaged_perceptron_tagger_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mlang\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    274\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(loc \u001b[38;5;241m+\u001b[39m TAGGER_JSONS[lang][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mweights\u001b[39m\u001b[38;5;124m\"\u001b[39m]) \u001b[38;5;28;01mas\u001b[39;00m fin:\n\u001b[0;32m    275\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mweights \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mload(fin)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\nltk\\data.py:579\u001b[0m, in \u001b[0;36mfind\u001b[1;34m(resource_name, paths)\u001b[0m\n\u001b[0;32m    577\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    578\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--> 579\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[93maveraged_perceptron_tagger_eng\u001b[0m not found.\n  Please use the NLTK Downloader to obtain the resource:\n\n  \u001b[31m>>> import nltk\n  >>> nltk.download('averaged_perceptron_tagger_eng')\n  \u001b[0m\n  For more information see: https://www.nltk.org/data.html\n\n  Attempted to load \u001b[93mtaggers/averaged_perceptron_tagger_eng/\u001b[0m\n\n  Searched in:\n    - 'C:\\\\Users\\\\nagar/nltk_data'\n    - 'C:\\\\Program Files\\\\WindowsApps\\\\PythonSoftwareFoundation.Python.3.12_3.12.2288.0_x64__qbz5n2kfra8p0\\\\nltk_data'\n    - 'C:\\\\Program Files\\\\WindowsApps\\\\PythonSoftwareFoundation.Python.3.12_3.12.2288.0_x64__qbz5n2kfra8p0\\\\share\\\\nltk_data'\n    - 'C:\\\\Program Files\\\\WindowsApps\\\\PythonSoftwareFoundation.Python.3.12_3.12.2288.0_x64__qbz5n2kfra8p0\\\\lib\\\\nltk_data'\n    - 'C:\\\\Users\\\\nagar\\\\AppData\\\\Roaming\\\\nltk_data'\n    - 'C:\\\\nltk_data'\n    - 'D:\\\\nltk_data'\n    - 'E:\\\\nltk_data'\n**********************************************************************\n"
     ]
    }
   ],
   "source": [
    "## We will find the Pos Tag\n",
    "\n",
    "for i in range(len(sentences)):\n",
    "    words=nltk.word_tokenize(sentences[i])\n",
    "    words=[word for word in words if word not in set(stopwords.words('english'))]\n",
    "    #sentences[i]=' '.join(words)# converting all the list of words into sentences\n",
    "    pos_tag=nltk.pos_tag(words)\n",
    "    print(pos_tag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Taj', 'Mahal', 'is', 'a', 'beautiful', 'Monument']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " \"Taj Mahal is a beautiful Monument\".split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('Taj', 'NNP'), ('Mahal', 'NNP'), ('is', 'VBZ'), ('a', 'DT'), ('beautiful', 'JJ'), ('Monument', 'NN')]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(nltk.pos_tag(\"Taj Mahal is a beautiful Monument\".split()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}