chapter_1/1.3/NLP_chapter_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Computing with Language: Simple Statistics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Introductory Examples for the NLTK Book ***\n",
      "Loading text1, ..., text9 and sent1, ..., sent9\n",
      "Type the name of the text or sentence to view it.\n",
      "Type: 'texts()' or 'sents()' to list the materials.\n",
      "text1: Moby Dick by Herman Melville 1851\n",
      "text2: Sense and Sensibility by Jane Austen 1811\n",
      "text3: The Book of Genesis\n",
      "text4: Inaugural Address Corpus\n",
      "text5: Chat Corpus\n",
      "text6: Monty Python and the Holy Grail\n",
      "text7: Wall Street Journal\n",
      "text8: Personals Corpus\n",
      "text9: The Man Who Was Thursday by G . K . Chesterton 1908\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk.book import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['After', 'all', 'is', 'said', 'and', 'done', 'more', 'is', 'said', 'than', 'done']\n",
      "['After', 'all', 'and', 'done', 'is', 'more', 'said', 'than']\n",
      "['said', 'than']\n"
     ]
    }
   ],
   "source": [
    "#recap of the previous topic\n",
    "saying = ['After','all','is','said','and','done','more','is','said','than','done']\n",
    "print(saying)\n",
    "tokens = set(saying)\n",
    "tokens = sorted(tokens)\n",
    "print(tokens)\n",
    "print(tokens[-2:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Frequency Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<FreqDist with 19317 samples and 260819 outcomes>\n",
      "\n",
      "\n",
      "printing first 50 words from text1 ::\n",
      "['[', 'Moby', 'Dick', 'by', 'Herman', 'Melville', '1851', ']', 'ETYMOLOGY', '.', '(', 'Supplied', 'a', 'Late', 'Consumptive', 'Usher', 'to', 'Grammar', 'School', ')', 'The', 'pale', '--', 'threadbare', 'in', 'coat', ',', 'heart', 'body', 'and', 'brain', ';', 'I', 'see', 'him', 'now', 'He', 'was', 'ever', 'dusting', 'his', 'old', 'lexicons', 'grammars', 'with', 'queer', 'handkerchief', 'mockingly', 'embellished', 'all']\n",
      "\n",
      "\n",
      "Frequency of \"Whale\" in the text1 ::\n",
      "906\n"
     ]
    }
   ],
   "source": [
    "#How can we automatically identify the words of a text that are most informative about the topic and genre of the text?\n",
    "#How to finding the 50 most frequent words in a book??\n",
    "fdist1 = FreqDist(text1)\n",
    "print(fdist1)\n",
    "print('\\n')\n",
    "#print first 50 words in the document\n",
    "print(\"printing first 50 words from text1 ::\")\n",
    "vocabulary1 = list(fdist1.keys())\n",
    "print(vocabulary1[:50])\n",
    "#print the frequency of 'whale'\n",
    "print('\\n')\n",
    "print('Frequency of \"Whale\" in the text1 ::')\n",
    "print(fdist1['whale'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f915ff92588>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's plot a commulative frequency plot\n",
    "fdist1.plot(50,cumulative=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total no of words occured only once in text document :: \n",
      "9002\n",
      "\n",
      "\n",
      "['Herman', 'Melville', ']', 'ETYMOLOGY', 'Late', 'Consumptive', 'School', 'threadbare', 'lexicons', 'mockingly', 'flags', 'mortality', 'signification', 'HACKLUYT', 'Sw', 'HVAL', 'roundness', 'Dut', 'Ger', 'WALLEN', 'WALW', 'IAN', 'RICHARDSON', 'KETOS', 'GREEK', 'CETUS', 'LATIN', 'WHOEL', 'ANGLO', 'SAXON', 'WAL', 'HWAL', 'SWEDISH', 'ICELANDIC', 'BALEINE', 'BALLENA', 'FEGEE', 'ERROMANGOAN', 'Librarian', 'painstaking', 'burrower', 'grub', 'Vaticans', 'stalls', 'higgledy', 'piggledy', 'gospel', 'promiscuously', 'commentator', 'belongest']\n"
     ]
    }
   ],
   "source": [
    "#if the frequent words don't helps, how about the words that occur once only ,the so called Hapaxes?\n",
    "print(\"total no of words occured only once in text document :: \")\n",
    "print(len(fdist1.hapaxes()))\n",
    "print(\"\\n\")\n",
    "print(fdist1.hapaxes()[:50])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine-Grained selection of words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['CIRCUMNAVIGATION', 'Physiognomically', 'apprehensiveness', 'cannibalistically', 'characteristically', 'circumnavigating', 'circumnavigation', 'circumnavigations', 'comprehensiveness', 'hermaphroditical', 'indiscriminately', 'indispensableness', 'irresistibleness', 'physiognomically', 'preternaturalness', 'responsibilities', 'simultaneousness', 'subterraneousness', 'supernaturalness', 'superstitiousness', 'uncomfortableness', 'uncompromisedness', 'undiscriminating', 'uninterpenetratingly']\n"
     ]
    }
   ],
   "source": [
    "#find out long words in the corpus\n",
    "#a.{w|w E V & p(w)}\n",
    "#b.[w for w in v if p(w)]\n",
    "v = set(text1)\n",
    "long_words = [w for w in v if len(w)>15]\n",
    "print(sorted(long_words))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['#14-19teens', '#talkcity_adults', '((((((((((', '........', 'Question', 'actually', 'anything', 'computer', 'cute.-ass', 'everyone', 'football', 'innocent', 'listening', 'remember', 'seriously', 'something', 'together', 'tomorrow', 'watching']\n"
     ]
    }
   ],
   "source": [
    "#find the words which have length greater then 7 and have frequency greater then 7 in text5\n",
    "fdist5 = FreqDist(text5)\n",
    "print(sorted([w for w in set(text5) if len(w)>7 and fdist5[w]>7]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collocations and Bigrams\n",
    " A collocation is a sequence of words that occure together unusually often.\n",
    " Example : Red Wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('more', 'is'), ('is', 'said'), ('said', 'than'), ('than', 'done')]\n"
     ]
    }
   ],
   "source": [
    "#to collect these kind of pair we can use bigram() function\n",
    "out  = bigrams(['more','is','said','than','done'])\n",
    "print(list(out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Colloccations are essentially just frequent bigrams.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['United States', 'fellow citizens', 'four years', 'years ago', 'Federal Government', 'General Government', 'American people', 'Vice President', 'God bless', 'Chief Justice', 'Old World', 'Almighty God', 'Fellow citizens', 'Chief Magistrate', 'every citizen', 'one another', 'fellow Americans', 'Indian tribes', 'public debt', 'foreign nations']\n"
     ]
    }
   ],
   "source": [
    "#text4.collocations() is not working now\n",
    "#so use the method collocation_list()\n",
    "print(text4.collocation_list())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Sperm Whale', 'Moby Dick', 'White Whale', 'old man', 'Captain Ahab', 'sperm whale', 'Right Whale', 'Captain Peleg', 'New Bedford', 'Cape Horn', 'cried Ahab', 'years ago', 'lower jaw', 'never mind', 'Father Mapple', 'cried Stubb', 'chief mate', 'white whale', 'ivory leg', 'one hand']\n"
     ]
    }
   ],
   "source": [
    "print(text1.collocation_list())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Counting other things"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 4, 2, 6, 8, 9, 11, 5, 7, 3, 10, 12, 13, 14, 16, 15, 17, 18, 20]\n"
     ]
    }
   ],
   "source": [
    "#print the frequently length of a word\n",
    "lenList1 = [len(w) for w in text1]\n",
    "fdist = FreqDist(lenList1)\n",
    "#print(fdist)\n",
    "#print all lengths of words used in text1\n",
    "print(list(fdist.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([(1, 47933), (4, 42345), (2, 38513), (6, 17111), (8, 9966), (9, 6428), (11, 1873), (5, 26597), (7, 14399), (3, 50223), (10, 3528), (12, 1053), (13, 567), (14, 177), (16, 22), (15, 70), (17, 12), (18, 1), (20, 1)])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the lenght and total no of words exist in corpus with that lenght\n",
    "fdist.items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3 : 50223'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the lenght which is used maximum number of times\n",
    "'{} : {}'.format(fdist.max(),fdist[fdist.max()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'19.255882431878046 %'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#percentage of that length in corpus\n",
    "'{} %'.format(100*fdist.freq(fdist.max()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a frequency distribution containing the given samples\n",
    "fdist = FreqDist(text1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count of the number of times a given sample occured\n",
    "fdist['monstrous']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.834076505162584e-05"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Frequency of a given sample\n",
    "fdist.freq('monstrous')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "260819"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total Number of samples\n",
    "fdist.N()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[', 'Moby', 'Dick', 'by', 'Herman', 'Melville', '1851', ']', 'ETYMOLOGY', '.']\n"
     ]
    }
   ],
   "source": [
    "#The samples sorted in order to decreasing frequency\n",
    "freList = list(fdist.keys())\n",
    "print(freList[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Iterate over the samples, in order  of decreasing frequency\n",
    "samples = []\n",
    "for sample in fdist:\n",
    "    samples.append(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "','"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sample with thr greatest count\n",
    "fdist.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tabulate the frequency distribution\n",
    "fdist.tabulate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f915ed87470>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Graphical plot of frequency distribution\n",
    "fdist.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f914f5d9898>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cumulative plot of the frequency distribution\n",
    "fdist.plot(cumulative=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Test if samples in fdist1 occur less frequently than in fdist2\n",
    "fdist < fdist1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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