[Project_002]_Natural_Language_Processing_(NLP)_Concepts.ipynb

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  "metadata": {
    "colab": {
      "name": "[Project 002] Natural Language Processing (NLP) Concepts.ipynb",
      "provenance": [],
      "collapsed_sections": [
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      "toc_visible": true,
      "authorship_tag": "ABX9TyMcD+4xMHTJE7uCp2Pj2KZ8",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/id-shiv/knowledge_base/blob/master/%5BProject_002%5D_Natural_Language_Processing_(NLP)_Concepts.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oJnzS4sXhVzy",
        "colab_type": "text"
      },
      "source": [
        "# Library"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lNlLNopQfjIo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import nltk\n",
        "from nltk.text import Text\n",
        "import matplotlib.pyplot as plt\n",
        "from wordcloud import WordCloud\n",
        "from nltk.corpus import wordnet as wn  # lexical database\n",
        "from nltk.stem import PorterStemmer\n",
        "from nltk.stem import WordNetLemmatizer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GkGhlKWZxtqP",
        "colab_type": "text"
      },
      "source": [
        "# Terminologies"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L5Ddm9sGxvHV",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "* Stop Words : Stop words are a set of commonly used words in any language. For example, in English, “the”, “is” and “and”, would easily qualify as stop words.\n",
        "* Synonyms : A synonym is a word or phrase that means the same (or very nearly the same) as another word or phrase.\n",
        "* Hypernyms : Y is a hypernym of X if every X is a (kind of) Y.\n",
        "* Hyponyms : Y is a hyponym of X if every Y is a (kind of) X.\n",
        "* Holonyms : Y is a holonym of X if X is a part of Y.\n",
        "* Antonyms : a word opposite in meaning to another (e.g. bad and good ).\n",
        "* Stemming : process of reducing a word to it's stem.\n",
        "* Lemmas (Lemmatization) : using morphological analysis of each word.\n",
        "* POS (Parts of Speech) : is the word tag in a sentense on whether it is a noun, verb, adverb, adjective ... \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1NgZ6iief_8c",
        "colab_type": "text"
      },
      "source": [
        "# NLTK"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EVjM9i0ih2ur",
        "colab_type": "text"
      },
      "source": [
        "## List the Corpuses"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lsgXCq5Bh5Wm",
        "colab_type": "code",
        "outputId": "1fe68fdf-1231-481d-8ab1-f82d58d9d447",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "nltk.download('all')  # enable this line if the nltk corpus is not downloaded_\n",
        "# in your environment\n",
        "\n",
        "# From Gutenberg Collection\n",
        "print(nltk.corpus.gutenberg.fileids())"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "[nltk_data]    | \n",
            "[nltk_data]  Done downloading collection all\n",
            "['austen-emma.txt', 'austen-persuasion.txt', 'austen-sense.txt', 'bible-kjv.txt', 'blake-poems.txt', 'bryant-stories.txt', 'burgess-busterbrown.txt', 'carroll-alice.txt', 'chesterton-ball.txt', 'chesterton-brown.txt', 'chesterton-thursday.txt', 'edgeworth-parents.txt', 'melville-moby_dick.txt', 'milton-paradise.txt', 'shakespeare-caesar.txt', 'shakespeare-hamlet.txt', 'shakespeare-macbeth.txt', 'whitman-leaves.txt']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2vWD1NyChTJg",
        "colab_type": "text"
      },
      "source": [
        "## Load a Sample Corpus"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L46R0eHchVR2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "imported_text = Text(nltk.corpus.gutenberg.words('carroll-alice.txt'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ldfPMhDBiOnm",
        "colab_type": "text"
      },
      "source": [
        "## NLTK Text Object"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-HLlyoy4iQMh",
        "colab_type": "code",
        "outputId": "55231904-03d6-43eb-8acf-339c54a59318",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(type(imported_text))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'nltk.text.Text'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4m8GnGOHj_hi",
        "colab_type": "text"
      },
      "source": [
        "### Number of Words"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2pwTiwf4kFH1",
        "colab_type": "code",
        "outputId": "0333f5f0-4d8f-4cb2-c9fd-1351a009b8f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(len(imported_text))  # includes punctutions"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "34110\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DtG2VNylkLbz",
        "colab_type": "text"
      },
      "source": [
        "### Number of unique words"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H2z6MQ-LkLkT",
        "colab_type": "code",
        "outputId": "de5a57db-7119-4f14-98cf-9052246a84d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(len(set(imported_text)))  # includes punctuations"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3016\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MBQ-FQzajckN",
        "colab_type": "text"
      },
      "source": [
        "### Concordance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pMq7e2LrjiTG",
        "colab_type": "code",
        "outputId": "a3037d9e-63d5-478c-9848-83aedc4ca07b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "source": [
        "print(imported_text.concordance(\"great\"))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Displaying 25 of 39 matches:\n",
            "led ' ORANGE MARMALADE ', but to her great disappointment it was empty : she di\n",
            " golden key in the lock , and to her great delight it fitted ! Alice opened the\n",
            " _I_ shan ' t be able ! I shall be a great deal too far off to trouble myself a\n",
            "amed of yourself ,' said Alice , ' a great girl like you ,' ( she might well sa\n",
            " other : he came trotting along in a great hurry , muttering to himself as he c\n",
            "n the world am I ? Ah , THAT ' S the great puzzle !' And she began thinking ove\n",
            " won ' t indeed !' said Alice , in a great hurry to change the subject of conve\n",
            " the Dodo could not answer without a great deal of thought , and it sat for a l\n",
            "since her swim in the pool , and the great hall , with the glass table and the \n",
            "knocking , and hurried upstairs , in great fear lest she should meet the real M\n",
            " over her head made her look up in a great hurry . An enormous puppy was lookin\n",
            "orry it ; then Alice dodged behind a great thistle , to keep herself from being\n",
            "e hanging out of its mouth , and its great eyes half shut . This seemed to Alic\n",
            "r drink something or other ; but the great question is , what ?' The great ques\n",
            " the great question is , what ?' The great question certainly was , what ? Alic\n",
            "an by producing from under his arm a great letter , nearly as large as himself \n",
            " sneezing , and every now and then a great crash , as if a dish or kettle had b\n",
            ": still it had VERY long claws and a great many teeth , so she felt that it oug\n",
            " ,' said Alice ; ' it ' s laid for a great many more than three .' ' Your hair \n",
            " looking at Alice for some time with great curiosity , and this was his first s\n",
            " the March Hare ,) '-- it was at the great concert given by the Queen of Hearts\n",
            "e sisters ,' the Dormouse began in a great hurry ; ' and their names were Elsie\n",
            "on ?' said Alice , who always took a great interest in questions of eating and \n",
            "han Alice could bear : she got up in great disgust , and walked off ; the Dormo\n",
            " fond of beheading people here ; the great wonder is , that there ' s any one l\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pMnGN67Nj107",
        "colab_type": "text"
      },
      "source": [
        "### Number of word occurance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qo5Kr3x9j19z",
        "colab_type": "code",
        "outputId": "795825d3-15eb-4229-ba2c-e56e0b3b8138",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print(imported_text.count('Alice'))  # input word is case-sensitive"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "396\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5j2JdtSxlU8F",
        "colab_type": "text"
      },
      "source": [
        "### Dispersion Plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Go6Bb7R_lVFq",
        "colab_type": "code",
        "outputId": "8f03e604-0a29-4b91-fe03-4853fb9022f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        }
      },
      "source": [
        "imported_text.dispersion_plot(['Alice', 'Rabbit', 'Hatter', 'Queen'])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2+59s2XdbpeffAS4Hvgt8B5hJcZ1iD+CA1Ox7ad584E7g71P4rAAuS+2eAQ6LYLlEP7A6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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-lihXNX1ly8W",
        "colab_type": "text"
      },
      "source": [
        "### Frequency Distribution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t45xfEPRlzLp",
        "colab_type": "code",
        "outputId": "c66326a4-0713-40dc-e6db-aacec05aaaf9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        }
      },
      "source": [
        "fdist = nltk.FreqDist(imported_text)\n",
        "fdist.plot(50, cumulative=True, title='Common Words')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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k/MItAFx/Rht+P6A7yxZGXLk1DCOOhONEhMNX7y3ysnJR1YEhxGU+6NUNH346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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8pG_ZFahmUwm",
        "colab_type": "text"
      },
      "source": [
        "#### Without punctuations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k1KOEdWNmXWI",
        "colab_type": "code",
        "outputId": "32ea89f9-60b5-48ab-c6ae-f3c5cbdf3d5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "fdist_no_punc = nltk.FreqDist(dict((word, freq) for word, freq in fdist.items() if word.isalpha()))\n",
        "fdist_no_punc.plot(50, cumulative=True, title='Common Words')"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YMwsgxtQTUQ2TEZGjRORK/7qtHw1lDMWl5fz+TRdAmqWn8MZvhtCrVYNEF8sY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\nRiQIgiBozf8AdEXSdT1b1osAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H_nVAvkEnNAE",
        "colab_type": "text"
      },
      "source": [
        "#### Without stopwords"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "derRP_b7nNIO",
        "colab_type": "code",
        "outputId": "1c792ce5-9b21-4cd1-814a-6ea48d90c802",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        }
      },
      "source": [
        "def stop_words():\n",
        "  return nltk.corpus.stopwords.words('english')\n",
        "\n",
        "def common_words_freq_dist(text, no_punctuations=True, no_stop_words=True):\n",
        "  common_words = list()\n",
        "  fdist = nltk.FreqDist(imported_text)\n",
        "  for word, freq in fdist.items():\n",
        "      if word.isalpha() and word not in stop_words():\n",
        "        common_words.append(word)\n",
        "  return nltk.FreqDist(common_words)\n",
        "\n",
        "common_words = common_words_freq_dist(imported_text)\n",
        "\n",
        "common_words.plot(50, cumulative=True, title='Common Words')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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M5bkrjqBXuyaVHGnsCzZz1zCMGkdVeW/uGkZNWMiW/GLSU5O59dQ+DEzbZEa/BjDDbxhG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\nYRc6tsRgAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m7Z-9-5zpmMZ",
        "colab_type": "text"
      },
      "source": [
        "### Hapaxes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UO6sbMGtpo-R",
        "colab_type": "text"
      },
      "source": [
        "Words that occur exactly once in the text"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9E_qLCBdpmV0",
        "colab_type": "code",
        "outputId": "5d4947d7-2699-46b6-f567-a13589ab48ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print(fdist.hapaxes())"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Lewis', 'Carroll', '1865', ']', 'Hole', 'conversations', 'daisy', 'chain', 'daisies', 'pink', 'wondered', 'actually', 'TOOK', 'WATCH', 'OUT', 'ITS', 'WAISTCOAT', 'POCKET', 'flashed', 'waistcoat', 'burning', 'field', 'fortunately', 'pop', 'tunnel', 'stopping', 'Either', 'plenty', 'maps', 'hung', 'pegs', 'labelled', 'ORANGE', 'MARMALADE', 'disappointment', 'empty', 'drop', 'killing', 'brave', 'centre', 'schoolroom', 'practice', 'yes', 'THROUGH', 'downward', 'Antipathies', 'country', 'New', 'Zealand', 'Australia', 'curtsey', 'CURTSEYING', '?)', 'ignorant', 'miss', 'saucer', 'dreamy', 'dozing', 'truth', 'heap', 'sticks', 'overhead', 'hurrying', 'lit', 'lamps', 'locked', 'Suddenly', 'solid', 'belong', 'curtain', 'fifteen', 'lock', 'fitted', 'rat', 'knelt', 'loveliest', 'wander', 'doorway', 'lately', 'telescopes', 'printed', 'letters', 'Drink', 'histories', 'burnt', 'eaten', 'poker', 'bleeds', 'disagree', 'flavour', 'cherry', 'tart', 'custard', 'pine', 'apple', 'roast', 'turkey', 'toffee', 'buttered', 'toast', 'shrink', 'flame', 'blown', 'plainly', 'climb', 'slippery', 'advise', 'advice', 'seldom', 'scolded', 'cheated', 'pretending', 'pretend', 'respectable', 'Soon', 'EAT', 'currants', 'creep', 'ate', \"?',\", 'eats', 'common', 'II', 'Pool', 'Tears', 'Curiouser', 'curiouser', 'largest', 'Good', 'stockings', 'able', ';--', 'Christmas', 'planning', 'carrier', 'odd', 'RIGHT', 'FOOT', 'ESQ', 'HEARTHRUG', 'NEAR', 'FENDER', 'WITH', 'LOVE', 'hopeless', 'Stop', 'shedding', 'gallons', 'reaching', 'dried', 'returning', 'splendidly', 'dressed', 'desperate', 'skurried', 'darkness', 'fanning', 'Dear', 'puzzle', 'Ada', 'Besides', 'five', 'thirteen', 'seven', 'Multiplication', 'Table', 'signify', 'Geography', 'London', 'crocodile', 'Improve', 'shining', 'pour', 'waters', 'Nile', 'scale', 'cheerfully', 'welcome', 'fishes', 'poky', 'toys', 'burst', 'measure', 'avoid', 'existence', 'speed', 'splash', 'somehow', 'seaside', 'coast', 'bathing', 'machines', 'sand', 'wooden', 'spades', 'lodging', 'houses', 'station', 'wept', 'punished', 'drowned', 'walrus', 'hippopotamus', 'harm', 'brother', 'Latin', 'Grammar', \"!')\", 'inquisitively', 'daresay', 'Ou', 'est', 'ma', 'chatte', 'leap', 'quiver', 'passionate', 'soothing', 'lazily', 'sits', 'licking', 'soft', 'bristling', 'Our', 'family', 'HATED', 'nasty', 'vulgar', 'eyed', 'terrier', 'curly', 'farmer', 'hundred', 'pounds', 'kills', 'rats', 'commotion', 'softly', 'III', 'Race', 'Long', 'Tale', 'draggled', 'feathers', 'clinging', 'dripping', 'consultation', 'familiarly', 'known', 'Indeed', 'positively', 'refused', 'Sit', 'LL', 'fixed', 'cold', 'Ahem', 'driest', 'favoured', 'pope', 'submitted', 'leaders', 'accustomed', 'usurpation', 'conquest', 'shiver', 'declared', 'Stigand', 'patriotic', 'Canterbury', 'crossly', 'worm', '\\'\"--', 'Edgar', 'Atheling', 'conduct', 'moderate', 'insolence', 'Normans', 'rising', 'meeting', 'adjourn', 'immediate', 'adoption', 'energetic', 'remedies', 'bent', 'hide', 'tittered', 'audibly', 'paused', 'SOMEBODY', 'inclined', 'winter', 'circle', 'exact', 'shape', 'placed', 'Shakespeare', 'EVERYBODY', 'calling', 'despair', 'pulled', 'luckily', 'prize', 'Hand', 'presented', 'acceptance', 'elegant', 'laugh', 'complained', 'theirs', 'ones', 'patted', 'begged', 'promised', 'C', 'Mine', 'prosecute', 'denial', 'cur', 'Such', 'Sir', 'cunning', 'condemn', 'death', 'fifth', 'undo', 'insult', 'growled', 'quicker', 'daughter', 'lose', 'snappishly', 'patience', 'oyster', 'addressing', 'pet', 'Magpie', 'wrapping', 'Canary', 'bed', 'various', 'pretexts', 'spirited', 'IV', 'Sends', 'Little', 'naturedly', 'completely', 'Run', 'Quick', 'pointed', 'housemaid', 'finds', 'neat', 'brass', 'W', 'RABBIT', 'engraved', 'upstairs', 'lest', 'fancying', 'Coming', 'tidy', 'hoped', 'pairs', 'nevertheless', 'uncorked', 'lips', 'SOMETHING', 'whenever', 'expected', 'pressing', 'ceiling', 'save', 'Alas', 'kneel', 'Still', 'resource', 'Luckily', 'magic', 'grew', 'pleasanter', 'rabbits', 'fairy', 'tales', 'HERE', 'comfort', 'foolish', 'Fetch', 'inwards', 'attempt', 'failure', 'possible', 'frame', 'Sounds', 'pronounced', 'arrum', \".')\", 'fills', 'whispers', 'coward', 'shrieks', 'frames', 'pulling', 'rumbling', 'cartwheels', 'ladder', 'lad', 'tie', 'rope', 'loose', 'Heads', 'Nay', 'Shy', 'fireplace', 'scratching', 'scrambling', 'Catch', 'Brandy', 'choke', 'Last', 'thank', 'ye', 'flustered', 'Jack', 'rocket', 'produced', 'SOME', 'swallowed', 'thick', 'arranged', 'peering', 'enormous', 'feebly', 'stretching', 'touch', 'whistle', 'terribly', 'spite', 'Hardly', 'whereupon', 'yelp', 'rushed', 'worry', 'dodged', 'tumbled', 'heels', 'cart', 'horse', 'trampled', 'series', 'charges', 'forwards', 'barking', 'hoarsely', 'faint', 'leant', 'buttercup', 'fanned', 'teaching', 'tricks', 'blades', 'circumstances', 'caterpillar', 'V', 'Advice', 'languid', 'shyly', 'sternly', 'MYSELF', 'clearly', 'sizes', 'chrysalis', 'butterfly', 'irritated', 'state', 'promising', 'swallowing', 'wait', 'puffed', 'HOW', 'DOTH', 'BUSY', 'BEE', 'Repeat', 'incessantly', 'son', 'feared', 'injure', 'brain', 'uncommonly', 'fat', 'sage', 'grey', 'limbs', 'supple', 'ointment', 'shilling', 'Allow', 'couple', 'tougher', 'suet', 'bones', 'beak', 'argued', 'wife', 'muscular', 'strength', 'jaw', 'Has', 'steady', 'balanced', 'awfully', 'airs', 'altered', 'DON', 'contradicted', 'losing', 'content', 'rearing', 'upright', 'piteous', 'crawled', 'underneath', 'closely', 'swallow', 'morsel', 'immense', 'length', 'rise', 'stalk', 'result', 'curving', 'graceful', 'zigzag', 'dive', 'tops', 'hiss', 'pigeon', 'flown', 'wings', 'subdued', 'sob', 'banks', 'hedges', 'pleasing', 'hatching', 'weeks', 'sorry', 'annoyed', 'highest', 'raising', 'needs', 'wriggling', 'invent', 'deepest', 'contempt', 'denying', 'egg', 'truthful', 'adding', 'YOURS', 'raw', 'nest', 'crouched', 'untwist', 'Whoever', 'frighten', 'wits', 'righthand', 'VI', '--(', 'judging', ')--', 'rapped', 'knuckles', 'footmen', 'powdered', 'crept', 'producing', 'Frog', 'From', 'curls', 'stupidly', 'knocked', 'reasons', 'inside', 'kettle', 'INSIDE', 'knock', 'uncivil', 'tomorrow', 'skimming', 'grazed', 'maybe', 'louder', 'argue', 'drive', 'crazy', 'variations', 'Anything', 'whistling', 'desperately', 'idiotic', 'smoke', 'stool', 'Even', 'sneezed', 'occasionally', 'alternately', 'hearth', 'grinning', 'manners', 'grins', 'violence', 'fix', 'irons', 'saucepans', 'blows', 'PLEASE', 'agony', 'terror', 'PRECIOUS', 'unusually', 'saucepan', 'flew', 'minded', 'axis', 'Talking', 'axes', 'chop', 'glanced', 'Twenty', 'bother', 'abide', 'figures', 'lullaby', 'shake', 'roughly', 'annoy', 'Because', 'teases', '):--', 'howled', 'enjoy', 'pleases', 'flinging', 'frying', 'pan', 'star', 'snorting', 'steam', 'engine', 'doubling', 'straightening', 'tight', 'prevent', 'undoing', 'kill', 'murder', 'grunt', 'expressing', 'snout', 'seriously', 'sobbed', 'NO', 'carry', 'relieved', 'trot', 'handsome', 'seeing', 'bough', 'yards', 'natured', 'teeth', 'treated', 'respect', 'Puss', 'wider', 'depends', 'SOMEWHERE', 'Visit', 'To', 'grant', 'growls', 'wags', 'wag', 'Therefore', 'growling', 'happening', 'hatters', 'branch', 'fig', 'appearing', 'vanishing', 'farther', 'chimneys', 'thatched', 'towards', 'VII', 'Mad', 'Tea', 'Party', 'elbows', 'PLENTY', 'chair', 'cutting', 'severity', 'raven', 'desk', 'ravens', 'desks', 'fourth', 'works', 'grumbled', 'gloomily', 'readily', 'stays', 'poured', 'riddle', 'slightest', 'Nor', 'wearily', 'waste', 'answers', 'accounts', 'terms', 'Half', 'mournfully', 'quarrelled', 'given', 'Like', 'tray', 'pinch', 'bawled', 'murdering', 'mournful', 'whiles', 'vote', 'alarmed', 'proposal', 'fellows', 'quick', 'Elsie', 'Lacie', 'Tillie', 'interest', 'eating', 'drinking', 'ways', 'LESS', 'opinion', 'helped', 'Sh', 'sh', 'interrupt', 'consented', 'promise', 'clean', 'unwillingly', 'jug', 'offend', 'eh', 'IN', 'choosing', 'doze', 'woke', 'traps', 'moon', 'memory', 'drawing', 'rudeness', 'disgust', 'teapot', 'THERE', 'stupidest', 'leading', 'today', 'unlocking', 'THEN', 'VIII', 'Croquet', 'Ground', 'entrance', 'Look', 'paint', 'jogged', 'Always', 'blame', 'deserved', 'spoken', 'tulip', 'onions', 'flung', 'brush', 'unjust', 'chanced', 'RED', 'afore', 'clubs', 'oblong', 'corners', 'diamonds', 'merrily', 'couples', 'hearts', 'Kings', 'Queens', 'recognised', 'noticing', 'velvet', 'KING', 'QUEEN', 'HEARTS', 'processions', 'opposite', 'Idiot', 'THESE', 'rosetree', 'pattern', 'backs', 'fury', 'glaring', 'beast', 'Nonsense', 'bowing', 'Leave', 'humble', 'meanwhile', 'examining', 'remaining', 'execute', 'protection', 'pot', 'marched', 'Their', 'evidently', 'roared', 'peeping', '!\"?\\'', 'boxed', 'laughter', 'hush', 'thunder', 'ridges', 'furrows', 'balls', 'mallets', 'double', 'chief', 'managing', 'comfortably', 'straightened', 'expression', 'bursting', 'laughing', 'provoking', 'act', 'crawling', 'ridge', 'furrow', 'send', 'doubled', 'parts', 'played', 'fighting', 'furious', 'uneasy', 'beheading', 'appearance', 'nodded', 'account', 'someone', 'fairly', 'complaining', 'quarrel', 'oneself', 'attends', 'arch', 'croqueted', 'win', 'finishing', 'kiss', 'likes', 'impertinent', 'king', 'passing', 'settling', 'difficulties', 'screaming', 'search', 'engaged', 'croqueting', 'helpless', 'appealed', 'settle', 'arguments', 'HIS', 'weren', 'prison', 'arrow', 'fading', 'IX', 'Story', 'affectionately', 'pleasant', 'hopeful', 'AT', 'Maybe', 'vinegar', 'sour', 'camomile', 'bitter', 'barley', 'sweet', 'stingy', 'Tut', 'tut', 'squeezed', 'closer', 'uncomfortably', 'bore', 'Somebody', 'minding', 'morals', 'waist', 'Birds', 'feather', 'flock', 'Right', 'mineral', 'attended', 'vegetable', 'imagine', 'cheap', 'birthday', 'Thinking', 'dig', 'worried', 'died', 'favourite', 'linked', 'tremble', 'thunderstorm', 'fair', 'warning', 'absence', 'shade', 'delay', 'cost', 'Those', 'whom', 'sentenced', 'company', 'pardoned', 'picture', 'lazy', 'leaving', 'rubbed', 'chuckled', 'executes', 'ledge', 'rock', 'pitied', 'hollow', 'EVEN', 'occasional', 'exclamation', 'Hjckrrh', 'calmly', 'sink', 'Drive', 'mayn', 'educations', 'VE', 'extras', 'learned', 'Certainly', 'OURS', 'bill', 'WASHING', 'extra', 'afford', 'inquired', 'Reeling', 'Writhing', 'Arithmetic', 'Ambition', 'Distraction', 'Derision', 'lifted', 'uglifying', 'beautify', 'prettier', 'uglify', 'simpleton', 'encouraged', 'counting', 'subjects', 'flappers', 'ancient', 'modern', 'Seaography', 'conger', 'Stretching', 'Fainting', 'Coils', 'stiff', 'Hadn', 'Classics', 'crab', 'Laughing', 'Grief', 'hid', 'Ten', 'lessen', 'eleventh', 'holiday', 'twelfth', 'games', 'X', 'flapper', 'Same', 'bone', 'punching', 'cheeks', 'introduced', \"')\", 'form', 'lines', 'Seals', 'salmon', 'cleared', 'jelly', 'Each', 'partner', 'partners', 'retire', 'bound', 'Swim', 'capering', 'Change', 'yelled', 'Back', 'land', 'dropping', 'pretty', 'forepaws', 'See', 'shingle', 'Too', 'askance', 'thanked', 'scaly', 'England', 'France', 'beloved', 'dinn', 'Dinn', \".--'\", 'thrown', 'DOES', 'BOOTS', 'SHOES', 'shiny', 'blacking', 'Boots', 'Soles', 'eels', 'shrimp', 'anywhere', 'Wouldn', 'journey', 'purpose', '\"?\\'', 'impatient', 'explanations', 'gained', 'listeners', 'Stand', '\"\\'', 'TIS', 'VOICE', 'SLUGGARD', 'baked', 'duck', 'eyelids', 'Trims', 'belt', 'buttons', 'sands', 'gay', 'lark', 'contemptuous', 'Shark', 'tide', 'rises', 'sharks', 'His', 'tremulous', '.]', 'uncommon', 'EVER', 'explained', 'disobey', 'sharing', 'crust', 'gravy', 'meat', 'share', 'treat', 'boon', 'Was', 'permitted', 'received', 'fork', 'banquet', '--]', 'Or', 'Hm', 'accounting', 'tastes', 'Sing', 'rich', 'Waiting', 'tureen', 'dainties', 'Game', 'beauti', 'FUL', 'SOUP', 'Chorus', 'panted', 'faintly', 'breeze', 'XI', 'Stole', 'Tarts', 'seated', 'throne', 'arrived', 'standing', 'chains', 'soldier', 'guard', 'refreshments', 'pass', 'justice', 'wore', 'frontispiece', 'comfortable', 'becoming', 'rightly', 'men', 'Stupid', 'indignant', 'spell', 'neighbour', 'muddle', 'squeaked', 'juror', 'Herald', 'accusation', 'stole', 'Fourteenth', 'Fifteenth', 'Sixteenth', 'Write', 'dates', 'reduced', 'shillings', 'pence', 'hat', 'Stolen', 'memorandum', 'hatter', 'fidgeted', 'spot', 'encourage', 'shifting', 'remain', 'squeeze', 'boldly', 'reasonable', 'pace', 'ridiculous', 'Bring', 'thin', 'dunce', 'twinkled', 'denies', 'speaker', 'canvas', 'bag', 'tied', 'strings', 'newspapers', 'trials', 'attempts', 'applause', 'understood', 'lower', 'SIT', 'officer', 'Shan', 'folding', 'Collar', 'shrieked', 'Behead', 'Suppress', 'Pinch', 'ache', 'fumbled', 'YET', 'Imagine', 'XII', 'Evidence', 'flurry', 'tipped', 'skirt', 'upsetting', 'sprawling', 'reminding', 'globe', 'accidentally', 'BEG', 'dismay', 'vague', 'die', 'cannot', 'emphasis', 'haste', 'downwards', 'unable', 'signifies', 'shock', 'pencils', 'diligently', 'overcome', 'gazing', 'WHATEVER', 'respectful', 'cackled', 'Rule', 'Forty', 'PERSONS', 'THAN', 'MILE', 'HIGH', 'LEAVE', 'COURT', 'mile', 'Nearly', 'invented', 'oldest', 'Number', 'OUTSIDE', 'handwriting', 'queerest', 'imitated', 'prove', 'sign', 'mischief', 'honest', 'clapping', 'PROVES', 'guilt', 'proves', 'Read', 'Begin', 'character', '):', 'push', 'returned', 'Though', 'Involved', 'affair', 'trusts', 'Before', 'fit', 'obstacle', 'Him', 'ourselves', 'secret', 'Between', 'sixpence', 'attempted', 'saves', 'spreading', 'SWIM', 'cardboard', 'WE', 'KNOW', 'BE', 'TRUE', 'RETURNED', 'FROM', 'clearer', 'BEFORE', 'HAD', 'fits', 'furiously', 'inkstand', 'ink', 'trickling', 'pun', 'consider', 'twentieth', 'Sentence', 'Stuff', 'purple', 'flying', 'brushing', 'fluttered', 'kissed', 'setting', 'dreaming', 'dreamed', 'clasped', 'toss', 'listened', 'rustled', 'splashed', 'neighbouring', 'rattle', 'shared', 'meal', 'crashed', 'choking', 'believed', 'reality', 'rustling', 'rippling', 'reeds', 'tinkling', 'sheep', 'bells', 'cries', 'shepherd', 'noises', 'clamour', 'busy', 'farm', 'yard', 'lowing', 'cattle', 'Lastly', 'pictured', 'riper', 'years', 'loving', 'childhood', 'gather', 'THEIR', 'sorrows', 'joys', 'remembering', 'happy']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f0PcNs4ap1fU",
        "colab_type": "text"
      },
      "source": [
        "### Collocation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CTsadFVup1pg",
        "colab_type": "text"
      },
      "source": [
        "A pair or group of words"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vhUe_BC7p17D",
        "colab_type": "code",
        "outputId": "e83de1a8-e4b4-4197-91ba-43cb4831073a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "print(imported_text.collocations())"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mock Turtle; said Alice; March Hare; White Rabbit; thought Alice;\n",
            "golden key; beautiful Soup; white kid; good deal; kid gloves; Mary\n",
            "Ann; yer honour; three gardeners; play croquet; Lobster Quadrille;\n",
            "ootiful Soo; great hurry; old fellow; trembling voice; poor little\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AYR41xFlvSK4",
        "colab_type": "text"
      },
      "source": [
        "## WordNet"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oAMDcXmSvv8q",
        "colab_type": "text"
      },
      "source": [
        "### Synonym sets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j7D_KeGevgS6",
        "colab_type": "code",
        "outputId": "a537973f-22fd-4e59-aa73-855512c0918a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 720
        }
      },
      "source": [
        "for synset in wn.synsets('Test'):\n",
        "  print(f'{str(synset)} ; \\n\\tdefinition : {synset.definition()} ; \\n\\texamples : {synset.examples()}')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Synset('trial.n.02') ; \n",
            "\tdefinition : trying something to find out about it ; \n",
            "\texamples : ['a sample for ten days free trial', 'a trial of progesterone failed to relieve the pain']\n",
            "Synset('test.n.02') ; \n",
            "\tdefinition : any standardized procedure for measuring sensitivity or memory or intelligence or aptitude or personality etc ; \n",
            "\texamples : ['the test was standardized on a large sample of students']\n",
            "Synset('examination.n.02') ; \n",
            "\tdefinition : a set of questions or exercises evaluating skill or knowledge ; \n",
            "\texamples : ['when the test was stolen the professor had to make a new set of questions']\n",
            "Synset('test.n.04') ; \n",
            "\tdefinition : the act of undergoing testing ; \n",
            "\texamples : ['he survived the great test of battle', 'candidates must compete in a trial of skill']\n",
            "Synset('test.n.05') ; \n",
            "\tdefinition : the act of testing something ; \n",
            "\texamples : ['in the experimental trials the amount of carbon was measured separately', 'he called each flip of the coin a new trial']\n",
            "Synset('test.n.06') ; \n",
            "\tdefinition : a hard outer covering as of some amoebas and sea urchins ; \n",
            "\texamples : []\n",
            "Synset('test.v.01') ; \n",
            "\tdefinition : put to the test, as for its quality, or give experimental use to ; \n",
            "\texamples : ['This approach has been tried with good results', 'Test this recipe']\n",
            "Synset('screen.v.01') ; \n",
            "\tdefinition : test or examine for the presence of disease or infection ; \n",
            "\texamples : ['screen the blood for the HIV virus']\n",
            "Synset('quiz.v.01') ; \n",
            "\tdefinition : examine someone's knowledge of something ; \n",
            "\texamples : ['The teacher tests us every week', 'We got quizzed on French irregular verbs']\n",
            "Synset('test.v.04') ; \n",
            "\tdefinition : show a certain characteristic when tested ; \n",
            "\texamples : ['He tested positive for HIV']\n",
            "Synset('test.v.05') ; \n",
            "\tdefinition : achieve a certain score or rating on a test ; \n",
            "\texamples : ['She tested high on the LSAT and was admitted to all the good law schools']\n",
            "Synset('test.v.06') ; \n",
            "\tdefinition : determine the presence or properties of (a substance) ; \n",
            "\texamples : []\n",
            "Synset('test.v.07') ; \n",
            "\tdefinition : undergo a test ; \n",
            "\texamples : [\"She doesn't test well\"]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vrQ78Q3cwPnN",
        "colab_type": "text"
      },
      "source": [
        "### Lemmas"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ir7dAdBWwRww",
        "colab_type": "text"
      },
      "source": [
        "Each synonym in the above synsets is called lemma"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "exwIBYefwWUo",
        "colab_type": "code",
        "outputId": "1ca1a1de-55bb-491e-c2cf-d7fddace6369",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(wn.synset('test.n.02').lemma_names())"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['test', 'mental_test', 'mental_testing', 'psychometric_test']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FZpfXgDeySON",
        "colab_type": "text"
      },
      "source": [
        "### Hyponyms"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_IvRLhP9yUmL",
        "colab_type": "code",
        "outputId": "f08acaed-b3ee-41f8-88ed-67200ec185c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "test = wn.synset('test.n.02')\n",
        "for synset in test.hyponyms():\n",
        "  for lemma in synset.lemmas():\n",
        "    print(lemma.name())"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "intelligence_test\n",
            "IQ_test\n",
            "personality_test\n",
            "sub-test\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ikw_ETf_zaOt",
        "colab_type": "text"
      },
      "source": [
        "### Hypernym"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KYJXMEOdzcFV",
        "colab_type": "code",
        "outputId": "2c6fa77d-5107-4b18-dea4-9aebcc7906c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "print(wn.synset('test.n.02'))\n",
        "print(wn.synset('test.n.02').hypernyms())\n",
        "print(wn.synset('test.n.02').hypernyms()[0].hypernyms())\n",
        "print(wn.synset('test.n.02').hypernyms()[0].hypernyms()[0].hypernyms())\n",
        "print(wn.synset('test.n.02').hypernyms()[0].hypernyms()[0].hypernyms()[0].hypernyms())\n",
        "print(wn.synset('test.n.02').hypernyms()[0].hypernyms()[0].hypernyms()[0].hypernyms()[0].hypernyms())\n",
        "print(wn.synset('test.n.02').hypernyms()[0].hypernyms()[0].hypernyms()[0].hypernyms()[0].hypernyms()[0].hypernyms())"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Synset('test.n.02')\n",
            "[Synset('mental_measurement.n.01')]\n",
            "[Synset('measurement.n.01')]\n",
            "[Synset('activity.n.01')]\n",
            "[Synset('act.n.02')]\n",
            "[Synset('event.n.01')]\n",
            "[Synset('psychological_feature.n.01')]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vLy_P_i00I3h",
        "colab_type": "text"
      },
      "source": [
        "### Word Similarity"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "59qt95vx0L_G",
        "colab_type": "text"
      },
      "source": [
        "#### Path Similarity"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VXnTGOEc0SI_",
        "colab_type": "text"
      },
      "source": [
        "Similarity in terms of distance between hypernyms and hyponyms"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R2c2aW6Q0bOD",
        "colab_type": "code",
        "outputId": "6442a6bb-0277-487f-ab27-afc25b81ef1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "word_1 = wn.synset('test.n.01')\n",
        "word_2 = wn.synset('test.n.01')\n",
        "print(word_1.path_similarity(word_2))\n",
        "\n",
        "word_1 = wn.synset('test.n.01')\n",
        "word_2 = wn.synset('try.v.02')\n",
        "print(word_1.path_similarity(word_2))\n",
        "\n",
        "word_1 = wn.synset('exam.n.01')\n",
        "word_2 = wn.synset('experiment.n.01')\n",
        "print('\\n-----------------------------')\n",
        "print(word_1.path_similarity(word_2))\n",
        "print(word_1.wup_similarity(word_2))\n",
        "print(word_1.lch_similarity(word_2))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0\n",
            "None\n",
            "\n",
            "-----------------------------\n",
            "0.1111111111111111\n",
            "0.5555555555555556\n",
            "1.4403615823901665\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BwJNS7INse9N",
        "colab_type": "text"
      },
      "source": [
        "## Word Cloud"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XtRwpZgssfG_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "text = 'Software testing is the process of verifying a system with the ' \\\n",
        "        'purpose of identifying any errors, gaps or missing requirement ' \\\n",
        "        'versus the actual requirement. Software testing is broadly ' \\\n",
        "        'categorised into two types - functional testing and non-functional ' \\\n",
        "        'testing.'\n",
        "word_cloud = WordCloud(stopwords=stop_words()).generate(text)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zhBhtRRXs7Gd",
        "colab_type": "text"
      },
      "source": [
        "### Plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "klbBPwIvs8Dt",
        "colab_type": "code",
        "outputId": "77afe370-c75c-4eba-d888-7a32c3c0d6dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "def plot_word_cloud(word_cloud):\n",
        "  plt.imshow(word_cloud)\n",
        "  plt.axis('off')\n",
        "  plt.show()\n",
        "\n",
        "plot_word_cloud(word_cloud)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hhQO5ZuQalT5BHbDnmLXLEfR1qzeHg+y/lb4s9C9o9vnJ5KzxNy0ce7180CTm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\niaUk55IAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "awh976xOLfcF",
        "colab_type": "text"
      },
      "source": [
        "## Stemming"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U1PsoKiALg86",
        "colab_type": "code",
        "outputId": "340233c8-eb1c-42ce-bff5-7e18a8502ea8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "stemmer = PorterStemmer()\n",
        "words = ['tested', 'testing', 'test', 'tests']\n",
        "\n",
        "for word in words:\n",
        "  print(f'Stem of the word {word} is {stemmer.stem(word)}')"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Stem of the word tested is test\n",
            "Stem of the word testing is test\n",
            "Stem of the word test is test\n",
            "Stem of the word tests is test\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6U94LBhsMXVC",
        "colab_type": "text"
      },
      "source": [
        "## Lemmatization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yZNaWkX0MiyV",
        "colab_type": "code",
        "outputId": "8d4b1b62-c2ef-4ddb-f139-7bd0f4fd296a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "lemmatizer = WordNetLemmatizer()\n",
        "word = 'better'\n",
        "\n",
        "print(lemmatizer.lemmatize(word, pos='v'))\n",
        "print(lemmatizer.lemmatize(word, pos='a'))\n",
        "print(lemmatizer.lemmatize(word, pos='r'))\n",
        "print(lemmatizer.lemmatize(word, pos='n'))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "better\n",
            "good\n",
            "well\n",
            "better\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0RnTCSqephyQ",
        "colab_type": "text"
      },
      "source": [
        "# SpaCy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ctwd7LX4pmRw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "0f07ff28-6232-4c07-ea3e-2eacc7958515"
      },
      "source": [
        "import spacy\n",
        "language_model = spacy.load('en')\n",
        "\n",
        "document = \"Hello!!, this is poc on how spacy works. lets explore natural language processing etc. in India here today\"\n",
        "\n",
        "loaded_doc = language_model(document)\n",
        "print(loaded_doc)\n",
        "\n",
        "cleaned_words = [word for word in loaded_doc if not word.is_stop and word.pos_ != 'PUNCT']\n",
        "print(cleaned_words)\n",
        "\n",
        "word_tags = [(word.lemma_, word.pos_, word.shape_, word.ent_type_, word.ent_iob_) for word in cleaned_words]\n",
        "print(word_tags)\n",
        "\n",
        "word_entities = [(word.text, word.label_) for word in loaded_doc.ents]\n",
        "print(word_entities)\n",
        "\n",
        "sentences = [(num, sentence) for num, sentence in enumerate(loaded_doc.sents)]\n",
        "print(sentences)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Hello!!, this is poc on how spacy works. lets explore natural language processing etc. in India here today\n",
            "[Hello, poc, spacy, works, lets, explore, natural, language, processing, etc, India, today]\n",
            "[('hello', 'INTJ', 'Xxxxx', '', 'O'), ('poc', 'NOUN', 'xxx', '', 'O'), ('spacy', 'NOUN', 'xxxx', '', 'O'), ('work', 'NOUN', 'xxxx', '', 'O'), ('let', 'VERB', 'xxxx', '', 'O'), ('explore', 'VERB', 'xxxx', '', 'O'), ('natural', 'ADJ', 'xxxx', '', 'O'), ('language', 'NOUN', 'xxxx', '', 'O'), ('processing', 'NOUN', 'xxxx', '', 'O'), ('etc', 'X', 'xxx', '', 'O'), ('India', 'PROPN', 'Xxxxx', 'GPE', 'B'), ('today', 'NOUN', 'xxxx', 'DATE', 'B')]\n",
            "[('India', 'GPE'), ('today', 'DATE')]\n",
            "[(0, Hello!!, this is poc on how spacy works.), (1, lets explore natural language processing etc.), (2, in India here today)]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}