NLP_Practice.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tokenization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "paragraph = \"\"\"After flying a long distance, a thirsty crow was wandering the forest in search of water. Finally, he saw a pot half-filled with water. \n",
    "He tried to drink from it but his beak wasn’t long enough to reach the water inside. He then saw pebbles on the ground and one by one, he put them in the pot until the water rose to the brim. \n",
    "The crow then hastily drank from it and quenched his thirst.\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = nltk.sent_tokenize(paragraph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['After flying a long distance, a thirsty crow was wandering the forest in search of water.', 'Finally, he saw a pot half-filled with water.', 'He tried to drink from it but his beak wasn’t long enough to reach the water inside.', 'He then saw pebbles on the ground and one by one, he put them in the pot until the water rose to the brim.', 'The crow then hastily drank from it and quenched his thirst.']\n"
     ]
    }
   ],
   "source": [
    "print(sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "word = nltk.word_tokenize(paragraph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['After', 'flying', 'a', 'long', 'distance', ',', 'a', 'thirsty', 'crow', 'was', 'wandering', 'the', 'forest', 'in', 'search', 'of', 'water', '.', 'Finally', ',', 'he', 'saw', 'a', 'pot', 'half-filled', 'with', 'water', '.', 'He', 'tried', 'to', 'drink', 'from', 'it', 'but', 'his', 'beak', 'wasn', '’', 't', 'long', 'enough', 'to', 'reach', 'the', 'water', 'inside', '.', 'He', 'then', 'saw', 'pebbles', 'on', 'the', 'ground', 'and', 'one', 'by', 'one', ',', 'he', 'put', 'them', 'in', 'the', 'pot', 'until', 'the', 'water', 'rose', 'to', 'the', 'brim', '.', 'The', 'crow', 'then', 'hastily', 'drank', 'from', 'it', 'and', 'quenched', 'his', 'thirst', '.']\n"
     ]
    }
   ],
   "source": [
    "print(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem import PorterStemmer\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\Sobhan\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "stemmer = PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['i',\n",
       " 'me',\n",
       " 'my',\n",
       " 'myself',\n",
       " 'we',\n",
       " 'our',\n",
       " 'ours',\n",
       " 'ourselves',\n",
       " 'you',\n",
       " \"you're\",\n",
       " \"you've\",\n",
       " \"you'll\",\n",
       " \"you'd\",\n",
       " 'your',\n",
       " 'yours',\n",
       " 'yourself',\n",
       " 'yourselves',\n",
       " 'he',\n",
       " 'him',\n",
       " 'his',\n",
       " 'himself',\n",
       " 'she',\n",
       " \"she's\",\n",
       " 'her',\n",
       " 'hers',\n",
       " 'herself',\n",
       " 'it',\n",
       " \"it's\",\n",
       " 'its',\n",
       " 'itself',\n",
       " 'they',\n",
       " 'them',\n",
       " 'their',\n",
       " 'theirs',\n",
       " 'themselves',\n",
       " 'what',\n",
       " 'which',\n",
       " 'who',\n",
       " 'whom',\n",
       " 'this',\n",
       " 'that',\n",
       " \"that'll\",\n",
       " 'these',\n",
       " 'those',\n",
       " 'am',\n",
       " 'is',\n",
       " 'are',\n",
       " 'was',\n",
       " 'were',\n",
       " 'be',\n",
       " 'been',\n",
       " 'being',\n",
       " 'have',\n",
       " 'has',\n",
       " 'had',\n",
       " 'having',\n",
       " 'do',\n",
       " 'does',\n",
       " 'did',\n",
       " 'doing',\n",
       " 'a',\n",
       " 'an',\n",
       " 'the',\n",
       " 'and',\n",
       " 'but',\n",
       " 'if',\n",
       " 'or',\n",
       " 'because',\n",
       " 'as',\n",
       " 'until',\n",
       " 'while',\n",
       " 'of',\n",
       " 'at',\n",
       " 'by',\n",
       " 'for',\n",
       " 'with',\n",
       " 'about',\n",
       " 'against',\n",
       " 'between',\n",
       " 'into',\n",
       " 'through',\n",
       " 'during',\n",
       " 'before',\n",
       " 'after',\n",
       " 'above',\n",
       " 'below',\n",
       " 'to',\n",
       " 'from',\n",
       " 'up',\n",
       " 'down',\n",
       " 'in',\n",
       " 'out',\n",
       " 'on',\n",
       " 'off',\n",
       " 'over',\n",
       " 'under',\n",
       " 'again',\n",
       " 'further',\n",
       " 'then',\n",
       " 'once',\n",
       " 'here',\n",
       " 'there',\n",
       " 'when',\n",
       " 'where',\n",
       " 'why',\n",
       " 'how',\n",
       " 'all',\n",
       " 'any',\n",
       " 'both',\n",
       " 'each',\n",
       " 'few',\n",
       " 'more',\n",
       " 'most',\n",
       " 'other',\n",
       " 'some',\n",
       " 'such',\n",
       " 'no',\n",
       " 'nor',\n",
       " 'not',\n",
       " 'only',\n",
       " 'own',\n",
       " 'same',\n",
       " 'so',\n",
       " 'than',\n",
       " 'too',\n",
       " 'very',\n",
       " 's',\n",
       " 't',\n",
       " 'can',\n",
       " 'will',\n",
       " 'just',\n",
       " 'don',\n",
       " \"don't\",\n",
       " 'should',\n",
       " \"should've\",\n",
       " 'now',\n",
       " 'd',\n",
       " 'll',\n",
       " 'm',\n",
       " 'o',\n",
       " 're',\n",
       " 've',\n",
       " 'y',\n",
       " 'ain',\n",
       " 'aren',\n",
       " \"aren't\",\n",
       " 'couldn',\n",
       " \"couldn't\",\n",
       " 'didn',\n",
       " \"didn't\",\n",
       " 'doesn',\n",
       " \"doesn't\",\n",
       " 'hadn',\n",
       " \"hadn't\",\n",
       " 'hasn',\n",
       " \"hasn't\",\n",
       " 'haven',\n",
       " \"haven't\",\n",
       " 'isn',\n",
       " \"isn't\",\n",
       " 'ma',\n",
       " 'mightn',\n",
       " \"mightn't\",\n",
       " 'mustn',\n",
       " \"mustn't\",\n",
       " 'needn',\n",
       " \"needn't\",\n",
       " 'shan',\n",
       " \"shan't\",\n",
       " 'shouldn',\n",
       " \"shouldn't\",\n",
       " 'wasn',\n",
       " \"wasn't\",\n",
       " 'weren',\n",
       " \"weren't\",\n",
       " 'won',\n",
       " \"won't\",\n",
       " 'wouldn',\n",
       " \"wouldn't\"]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_len = stopwords.words('english')\n",
    "len(list_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "paragraph_2 = \"\"\"Once there lived a greedy man in a small town. He was very rich, and he loved gold and all things fancy. \n",
    "But he loved his daughter more than anything.\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = nltk.sent_tokenize(paragraph_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Once there lived a greedy man in a small town.', 'He was very rich, and he loved gold and all things fancy.', 'But he loved his daughter more than anything.']\n"
     ]
    }
   ],
   "source": [
    "print(sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "paragraph_tokens = word_tokenize(paragraph_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokens_without_sw = [word for word in paragraph_tokens if not word in stopwords.words()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Once', 'lived', 'greedy', 'small', 'town', '.', 'He', 'rich', ',', 'loved', 'gold', 'things', 'fancy', '.', 'But', 'loved', 'daughter', '.']\n"
     ]
    }
   ],
   "source": [
    "print(tokens_without_sw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lemmatization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem import WordNetLemmatizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "lemmatizer = WordNetLemmatizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(sentence)):\n",
    "    words = nltk.word_tokenize(sentence[i])\n",
    "    words =  [lemmatizer.lemmatize(word) for word in words if word not in set(stopwords.words('english'))]\n",
    "    sentence[i] = ' '.join(words)\n",
    "  \n",
    "\n",
    "\n",
    "#print(lemmatizer.lemmatize(\"word\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Once lived greedy man small town .', 'He rich , loved gold thing fancy .', 'But loved daughter anything .']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(sentence)\n",
    "len(sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# stemming"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PorterStemmer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk.stem import PorterStemmer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "porter = PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "words = ['Connects','Connecting','Connections','Connected','Connection','Connectings','Connect']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connects ----- connect\n",
      "Connecting ----- connect\n",
      "Connections ----- connect\n",
      "Connected ----- connect\n",
      "Connection ----- connect\n",
      "Connectings ----- connect\n",
      "Connect ----- connect\n"
     ]
    }
   ],
   "source": [
    "for word in words:\n",
    "    print(word,\"-----\",porter.stem(word))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(porter.stem(\"words\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TF_IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "paragraph_3 = \"\"\"Once there was a hungry fox who stumbled upon a vineyard. After seeing the round, juicy grapes hanging in a bunch, the fox drooled. \n",
    "But no matter how high he jumped, he couldn’t reach for it. So he told himself that it was probably sour and left. \n",
    "That night, he had to sleep on an empty stomach.\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem.porter import PorterStemmer\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "import re\n",
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpors = []\n",
    "ps = PorterStemmer()\n",
    "wordnet = WordNetLemmatizer()\n",
    "sentences = nltk.sent_tokenize(paragraph_3)\n",
    "for i in range(len(sentences)):\n",
    "    review = re.sub('[^a-zA-Z]',' ', sentences[i])\n",
    "    review = review.lower()\n",
    "    review = review.split()\n",
    "    review = [wordnet.lemmatize(word) for word in review if word not in set(stopwords.words('english'))]\n",
    "    review = ' '.join(review)\n",
    "    corpors.append(review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         0.         0.         0.37410477 0.         0.\n",
      "  0.         0.46369322 0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.46369322 0.         0.46369322 0.46369322]\n",
      " [0.36152912 0.36152912 0.         0.29167942 0.36152912 0.36152912\n",
      "  0.         0.         0.36152912 0.         0.         0.\n",
      "  0.         0.         0.         0.36152912 0.36152912 0.\n",
      "  0.         0.         0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.5        0.         0.         0.5        0.         0.5\n",
      "  0.         0.         0.5        0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.5        0.\n",
      "  0.         0.5        0.         0.         0.         0.\n",
      "  0.5        0.         0.         0.5        0.         0.        ]\n",
      " [0.         0.         0.5        0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.5        0.         0.         0.         0.         0.5\n",
      "  0.         0.5        0.         0.         0.         0.        ]]\n"
     ]
    }
   ],
   "source": [
    "cv = TfidfVectorizer()\n",
    "X = cv.fit_transform(corpors).toarray()\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bag of Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "paragraph_4 = \"\"\"Long ago, there lived the Pandavas and the Kauravas, the princes of Hastinapura. All the princes were taught archery by sage Dronacharya. \n",
    "The Pandava prince, Arjuna, was Drona’s favourite student. Ekalavya, a poor commoner boy, also wanted to be Drona’s pupil but Drona refused to teach him. \n",
    "Ekalavya was a determined boy. He carved a statue of Drona on a tree-trunk in the forest and started practising in front of it. Time went by. One day, the princes and their teacher came to the same forest. \n",
    "As Arjuna aimed at a particularly difficult target, an arrow pierced the target. Shocked, the boys and their teacher looked around. They saw Ekalavya, who went up to touch Drona’s feet. “Who is your teacher?” \n",
    "Drona asked. Ekalavya quietly led him to the statue. Drona did not want anyone to be better than Arjun. He thought for some time and said, “If I am your guru, give me my gurudakshina.” \n",
    "“Surely, sir,” bowed Ekalavya. “I want your right thumb,” replied Drona. Ekalavya wordlessly cut off his right thumb and laid it at Drona’s feet.\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cleaning the texts\n",
    "\n",
    "import re\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "from nltk.stem import WordNetLemmatizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "ps = PorterStemmer()\n",
    "wordnet = WordNetLemmatizer()\n",
    "\n",
    "sentences = nltk.sent_tokenize(paragraph_4)\n",
    "\n",
    "#sentences = nltk.sent_tokenizer(para)\n",
    "\n",
    "corpus = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(sentences)):\n",
    "    review = re.sub('[^a-zA-Z]',' ', sentences[i])\n",
    "    review = review.lower()\n",
    "    review = review.split()\n",
    "    review = [wordnet.lemmatize(word) for word in review if word not in set(stopwords.words('english'))]\n",
    "    review = ' '.join(review)\n",
    "    corpus.append(review)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['long ago lived pandavas kauravas prince hastinapura', 'prince taught archery sage dronacharya', 'pandava prince arjuna drona favourite student', 'ekalavya poor commoner boy also wanted drona pupil drona refused teach', 'ekalavya determined boy', 'carved statue drona tree trunk forest started practising front', 'time went', 'one day prince teacher came forest', 'arjuna aimed particularly difficult target arrow pierced target', 'shocked boy teacher looked around', 'saw ekalavya went touch drona foot', 'teacher drona asked', 'ekalavya quietly led statue', 'drona want anyone better arjun', 'thought time said guru give gurudakshina surely sir bowed ekalavya', 'want right thumb replied drona', 'ekalavya wordlessly cut right thumb laid drona foot']\n"
     ]
    }
   ],
   "source": [
    "print(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 1]]\n"
     ]
    }
   ],
   "source": [
    "#creating the bag of word\n",
    "\n",
    "cv = CountVectorizer()\n",
    "X = cv.fit_transform(corpus).toarray()\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parts of Speech Tag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk import pos_tag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"Hii Noel, How are you doing? I will be travelling to your city. Lest meet one day.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Hii', 'NNP'),\n",
       " ('Noel', 'NNP'),\n",
       " (',', ','),\n",
       " ('How', 'NNP'),\n",
       " ('are', 'VBP'),\n",
       " ('you', 'PRP'),\n",
       " ('doing', 'VBG'),\n",
       " ('?', '.'),\n",
       " ('I', 'PRP'),\n",
       " ('will', 'MD'),\n",
       " ('be', 'VB'),\n",
       " ('travelling', 'VBG'),\n",
       " ('to', 'TO'),\n",
       " ('your', 'PRP$'),\n",
       " ('city', 'NN'),\n",
       " ('.', '.'),\n",
       " ('Lest', 'NNP'),\n",
       " ('meet', 'VBD'),\n",
       " ('one', 'CD'),\n",
       " ('day', 'NN'),\n",
       " ('.', '.')]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokens = word_tokenize(text)\n",
    "pos_tag(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import wordnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('good.n.01'),\n",
       " Synset('good.n.02'),\n",
       " Synset('good.n.03'),\n",
       " Synset('commodity.n.01'),\n",
       " Synset('good.a.01'),\n",
       " Synset('full.s.06'),\n",
       " Synset('good.a.03'),\n",
       " Synset('estimable.s.02'),\n",
       " Synset('beneficial.s.01'),\n",
       " Synset('good.s.06'),\n",
       " Synset('good.s.07'),\n",
       " Synset('adept.s.01'),\n",
       " Synset('good.s.09'),\n",
       " Synset('dear.s.02'),\n",
       " Synset('dependable.s.04'),\n",
       " Synset('good.s.12'),\n",
       " Synset('good.s.13'),\n",
       " Synset('effective.s.04'),\n",
       " Synset('good.s.15'),\n",
       " Synset('good.s.16'),\n",
       " Synset('good.s.17'),\n",
       " Synset('good.s.18'),\n",
       " Synset('good.s.19'),\n",
       " Synset('good.s.20'),\n",
       " Synset('good.s.21'),\n",
       " Synset('well.r.01'),\n",
       " Synset('thoroughly.r.02')]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wordnet.synsets(\"good\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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