01_language_processing_and_python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Language Processing and Python\n",
    "\n",
    "## Computing with Language: Texts and Words\n",
    "\n",
    "Ran the following in `python3` interpreter:\n",
    "\n",
    "    import nltk\n",
    "    nltk.download()\n",
    "\n",
    "Select *book* to download corpora for NLTK Book"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "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": [
    "from nltk.book import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Text: Moby Dick by Herman Melville 1851>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Text: Sense and Sensibility by Jane Austen 1811>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "**`concordance`** is a view that shows every occurrence of a word alongside some context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Displaying 11 of 11 matches:\n",
      "ong the former , one was of a most monstrous size . ... This came towards us , \n",
      "ON OF THE PSALMS . \" Touching that monstrous bulk of the whale or ork we have r\n",
      "ll over with a heathenish array of monstrous clubs and spears . Some were thick\n",
      "d as you gazed , and wondered what monstrous cannibal and savage could ever hav\n",
      "that has survived the flood ; most monstrous and most mountainous ! That Himmal\n",
      "they might scout at Moby Dick as a monstrous fable , or still worse and more de\n",
      "th of Radney .'\" CHAPTER 55 Of the Monstrous Pictures of Whales . I shall ere l\n",
      "ing Scenes . In connexion with the monstrous pictures of whales , I am strongly\n",
      "ere to enter upon those still more monstrous stories of them which are to be fo\n",
      "ght have been rummaged out of this monstrous cabinet there is no telling . But \n",
      "of Whale - Bones ; for Whales of a monstrous size are oftentimes cast up dead u\n"
     ]
    }
   ],
   "source": [
    "text1.concordance(\"monstrous\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Displaying 25 of 79 matches:\n",
      ", however , and , as a mark of his affection for the three girls , he left them\n",
      "t . It was very well known that no affection was ever supposed to exist between\n",
      "deration of politeness or maternal affection on the side of the former , the tw\n",
      "d the suspicion -- the hope of his affection for me may warrant , without impru\n",
      "hich forbade the indulgence of his affection . She knew that his mother neither\n",
      "rd she gave one with still greater affection . Though her late conversation wit\n",
      " can never hope to feel or inspire affection again , and if her home be uncomfo\n",
      "m of the sense , elegance , mutual affection , and domestic comfort of the fami\n",
      ", and which recommended him to her affection beyond every thing else . His soci\n",
      "ween the parties might forward the affection of Mr . Willoughby , an equally st\n",
      " the most pointed assurance of her affection . Elinor could not be surprised at\n",
      "he natural consequence of a strong affection in a young and ardent mind . This \n",
      " opinion . But by an appeal to her affection for her mother , by representing t\n",
      " every alteration of a place which affection had established as perfect with hi\n",
      "e will always have one claim of my affection , which no other can possibly shar\n",
      "f the evening declared at once his affection and happiness . \" Shall we see you\n",
      "ause he took leave of us with less affection than his usual behaviour has shewn\n",
      "ness .\" \" I want no proof of their affection ,\" said Elinor ; \" but of their en\n",
      "onths , without telling her of his affection ;-- that they should part without \n",
      "ould be the natural result of your affection for her . She used to be all unres\n",
      "distinguished Elinor by no mark of affection . Marianne saw and listened with i\n",
      "th no inclination for expense , no affection for strangers , no profession , an\n",
      "till distinguished her by the same affection which once she had felt no doubt o\n",
      "al of her confidence in Edward ' s affection , to the remembrance of every mark\n",
      " was made ? Had he never owned his affection to yourself ?\" \" Oh , no ; but if \n"
     ]
    }
   ],
   "source": [
    "text2.concordance(\"affection\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Displaying 25 of 38 matches:\n",
      "ay when they were created . And Adam lived an hundred and thirty years , and be\n",
      "ughters : And all the days that Adam lived were nine hundred and thirty yea and\n",
      "nd thirty yea and he died . And Seth lived an hundred and five years , and bega\n",
      "ve years , and begat Enos : And Seth lived after he begat Enos eight hundred an\n",
      "welve years : and he died . And Enos lived ninety years , and begat Cainan : An\n",
      " years , and begat Cainan : And Enos lived after he begat Cainan eight hundred \n",
      "ive years : and he died . And Cainan lived seventy years and begat Mahalaleel :\n",
      "rs and begat Mahalaleel : And Cainan lived after he begat Mahalaleel eight hund\n",
      "years : and he died . And Mahalaleel lived sixty and five years , and begat Jar\n",
      "s , and begat Jared : And Mahalaleel lived after he begat Jared eight hundred a\n",
      "and five yea and he died . And Jared lived an hundred sixty and two years , and\n",
      "o years , and he begat Eno And Jared lived after he begat Enoch eight hundred y\n",
      " and two yea and he died . And Enoch lived sixty and five years , and begat Met\n",
      " ; for God took him . And Methuselah lived an hundred eighty and seven years , \n",
      " , and begat Lamech . And Methuselah lived after he begat Lamech seven hundred \n",
      "nd nine yea and he died . And Lamech lived an hundred eighty and two years , an\n",
      "ch the LORD hath cursed . And Lamech lived after he begat Noah five hundred nin\n",
      "naan shall be his servant . And Noah lived after the flood three hundred and fi\n",
      "xad two years after the flo And Shem lived after he begat Arphaxad five hundred\n",
      "at sons and daughters . And Arphaxad lived five and thirty years , and begat Sa\n",
      "ars , and begat Salah : And Arphaxad lived after he begat Salah four hundred an\n",
      "begat sons and daughters . And Salah lived thirty years , and begat Eber : And \n",
      "y years , and begat Eber : And Salah lived after he begat Eber four hundred and\n",
      " begat sons and daughters . And Eber lived four and thirty years , and begat Pe\n",
      "y years , and begat Peleg : And Eber lived after he begat Peleg four hundred an\n"
     ]
    }
   ],
   "source": [
    "text3.concordance(\"lived\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "**`similar`** shows other words that appear in a similar context to the entered word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trustworthy mean impalpable true singular reliable wise doleful\n",
      "domineering passing uncommon contemptible delightfully exasperate\n",
      "gamesome mystifying part imperial maddens determined\n"
     ]
    }
   ],
   "source": [
    "text1.similar(\"monstrous\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "very exceedingly so heartily as remarkably great vast extremely good a\n",
      "amazingly sweet\n"
     ]
    }
   ],
   "source": [
    "text2.similar(\"monstrous\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*text 1 (Melville) uses **monstrous** very differently from text 2 (Austen)*\n",
    "\n",
    "* Text 2: **monstrous** has positive connotations, sometimes functions as an intensifier like *very*\n",
    "\n",
    "---\n",
    "\n",
    "**`common_contexts`** shows contexts that are shared by two or more words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "be_glad a_pretty am_glad is_pretty a_lucky\n"
     ]
    }
   ],
   "source": [
    "text2.common_contexts([\"monstrous\", \"very\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*trying out other words...*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attention regard time love mother heart sister wife kindness opinion\n",
      "arrival marianne wishes visit behaviour engagement letter brother\n",
      "elinor marriage\n"
     ]
    }
   ],
   "source": [
    "text2.similar(\"affection\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "my_for her_and his_for her_for continued_for the_of your_for no_for\n",
      "his_and\n"
     ]
    }
   ],
   "source": [
    "text2.common_contexts([\"affection\", \"regard\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "**Lexical Dispersion Plot**\n",
    "\n",
    "Determining the location of words in text (how many words from beginning does this word appear?) -- using **`dispersion_plot`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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lTvmLkty9s/7cieVPJLnRsPy0JE+bZ5z7949+jwOtiWCr+3hW2bXqrbV8xhlrtzur/fXG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\nAGDG/wOAdF2JBoCM9wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11be9f4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,10))\n",
    "text4.dispersion_plot([\"citizens\", \"democracy\", \"freedom\", \"duties\", \"America\", \"liberty\", \"constitution\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generating some random text in the style of *text3* -- using **`generate()`**\n",
    "\n",
    "**not yet supported in NLTK 3.0**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# (not available in NLTK 3.0)\n",
    "\n",
    "# text3.generate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Counting Vocabulary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Count the number of tokens using **`len`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44764"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(text3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View/count vocabulary using **`set(text_obj)`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2789"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(text3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['!',\n",
       " \"'\",\n",
       " '(',\n",
       " ')',\n",
       " ',',\n",
       " ',)',\n",
       " '.',\n",
       " '.)',\n",
       " ':',\n",
       " ';',\n",
       " ';)',\n",
       " '?',\n",
       " '?)',\n",
       " 'A',\n",
       " 'Abel',\n",
       " 'Abelmizraim',\n",
       " 'Abidah',\n",
       " 'Abide',\n",
       " 'Abimael',\n",
       " 'Abimelech',\n",
       " 'Abr',\n",
       " 'Abrah',\n",
       " 'Abraham',\n",
       " 'Abram',\n",
       " 'Accad',\n",
       " 'Achbor',\n",
       " 'Adah',\n",
       " 'Adam',\n",
       " 'Adbeel',\n",
       " 'Admah',\n",
       " 'Adullamite',\n",
       " 'After',\n",
       " 'Aholibamah',\n",
       " 'Ahuzzath',\n",
       " 'Ajah',\n",
       " 'Akan',\n",
       " 'All',\n",
       " 'Allonbachuth',\n",
       " 'Almighty',\n",
       " 'Almodad',\n",
       " 'Also',\n",
       " 'Alvah',\n",
       " 'Alvan',\n",
       " 'Am',\n",
       " 'Amal',\n",
       " 'Amalek',\n",
       " 'Amalekites',\n",
       " 'Ammon',\n",
       " 'Amorite',\n",
       " 'Amorites']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first 50\n",
    "sorted(set(text3))[:50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "---\n",
    "Calculating **lexical richness** of the text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06230453042623537"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(text3)) / len(text3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Count how often a word occurs in the text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text3.count(\"smote\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Compute what percentage of the text is taken up by a specific word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4643016433938312"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100 * text4.count('a') / len(text4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "704"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text5.count('lol')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5640968673628082"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100 * text5.count('lol') / len(text5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Define some simple functions to calculate these values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def lexical_diversity(text):\n",
    "    return len(set(text)) / len(text)\n",
    "\n",
    "def percentage(count, total):\n",
    "    return 100 * count / total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.06230453042623537, 0.13477005109975562)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lexical_diversity(text3), lexical_diversity(text5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4643016433938312"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentage(text4.count('a'), len(text4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Closer Look at Python: Texts as Lists of Words\n",
    "\n",
    "*skipping some basic python parts of this section...*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Call', 'me', 'Ishmael', '.']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['The',\n",
       " 'family',\n",
       " 'of',\n",
       " 'Dashwood',\n",
       " 'had',\n",
       " 'long',\n",
       " 'been',\n",
       " 'settled',\n",
       " 'in',\n",
       " 'Sussex',\n",
       " '.']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lexical_diversity(sent1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "List Concatenation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Monty', 'Python', 'and', 'the', 'Holy', 'Grail']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "['Monty', 'Python'] + ['and', 'the', 'Holy', 'Grail']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Indexing Lists (...and Text objects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'awaken'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text4[173]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text4.index('awaken')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['U86',\n",
       " 'thats',\n",
       " 'why',\n",
       " 'something',\n",
       " 'like',\n",
       " 'gamefly',\n",
       " 'is',\n",
       " 'so',\n",
       " 'good',\n",
       " 'because',\n",
       " 'you',\n",
       " 'can',\n",
       " 'actually',\n",
       " 'play',\n",
       " 'a',\n",
       " 'full',\n",
       " 'game',\n",
       " 'without',\n",
       " 'buying',\n",
       " 'it']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text5[16715:16735]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['We',\n",
       " \"'\",\n",
       " 're',\n",
       " 'an',\n",
       " 'anarcho',\n",
       " '-',\n",
       " 'syndicalist',\n",
       " 'commune',\n",
       " '.',\n",
       " 'We',\n",
       " 'take',\n",
       " 'it',\n",
       " 'in',\n",
       " 'turns',\n",
       " 'to',\n",
       " 'act',\n",
       " 'as',\n",
       " 'a',\n",
       " 'sort',\n",
       " 'of',\n",
       " 'executive',\n",
       " 'officer',\n",
       " 'for',\n",
       " 'the',\n",
       " 'week']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text6[1600:1625]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing with Language: Simple Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['said', 'than']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "saying = 'After all is said and done more is said than done'.split()\n",
    "tokens = sorted(set(saying))\n",
    "tokens[-2:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "**Frequency Distributions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<FreqDist with 19317 samples and 260819 outcomes>\n"
     ]
    }
   ],
   "source": [
    "fdist1 = FreqDist(text1)\n",
    "print(fdist1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(',', 18713),\n",
       " ('the', 13721),\n",
       " ('.', 6862),\n",
       " ('of', 6536),\n",
       " ('and', 6024),\n",
       " ('a', 4569),\n",
       " ('to', 4542),\n",
       " (';', 4072),\n",
       " ('in', 3916),\n",
       " ('that', 2982),\n",
       " (\"'\", 2684),\n",
       " ('-', 2552),\n",
       " ('his', 2459),\n",
       " ('it', 2209),\n",
       " ('I', 2124),\n",
       " ('s', 1739),\n",
       " ('is', 1695),\n",
       " ('he', 1661),\n",
       " ('with', 1659),\n",
       " ('was', 1632),\n",
       " ('as', 1620),\n",
       " ('\"', 1478),\n",
       " ('all', 1462),\n",
       " ('for', 1414),\n",
       " ('this', 1280),\n",
       " ('!', 1269),\n",
       " ('at', 1231),\n",
       " ('by', 1137),\n",
       " ('but', 1113),\n",
       " ('not', 1103),\n",
       " ('--', 1070),\n",
       " ('him', 1058),\n",
       " ('from', 1052),\n",
       " ('be', 1030),\n",
       " ('on', 1005),\n",
       " ('so', 918),\n",
       " ('whale', 906),\n",
       " ('one', 889),\n",
       " ('you', 841),\n",
       " ('had', 767),\n",
       " ('have', 760),\n",
       " ('there', 715),\n",
       " ('But', 705),\n",
       " ('or', 697),\n",
       " ('were', 680),\n",
       " ('now', 646),\n",
       " ('which', 640),\n",
       " ('?', 637),\n",
       " ('me', 627),\n",
       " ('like', 624)]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdist1.most_common(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "906"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdist1['whale']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*50 most frequent words account for almost half of the book*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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szPlKktSo3KHhDg1JkhpaczOMHVscYDz9NLS0tP3MVlsVhxe77JJed2n461wl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\nkiRJkqSx8/8DPlDBngHlC9IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11be9f588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,10))\n",
    "fdist1.plot(50, cumulative=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fine-grained Selection of Words**\n",
    "\n",
    "Looking at *long* words of a text (maybe these will be more meaningful words?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CIRCUMNAVIGATION',\n",
       " 'Physiognomically',\n",
       " 'apprehensiveness',\n",
       " 'cannibalistically',\n",
       " 'characteristically',\n",
       " 'circumnavigating',\n",
       " 'circumnavigation',\n",
       " 'circumnavigations',\n",
       " 'comprehensiveness',\n",
       " 'hermaphroditical',\n",
       " 'indiscriminately',\n",
       " 'indispensableness',\n",
       " 'irresistibleness',\n",
       " 'physiognomically',\n",
       " 'preternaturalness',\n",
       " 'responsibilities',\n",
       " 'simultaneousness',\n",
       " 'subterraneousness',\n",
       " 'supernaturalness',\n",
       " 'superstitiousness',\n",
       " 'uncomfortableness',\n",
       " 'uncompromisedness',\n",
       " 'undiscriminating',\n",
       " 'uninterpenetratingly']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "V = set(text1)\n",
    "long_words = [w for w in V if len(w) > 15]\n",
    "\n",
    "sorted(long_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "*words that are longer than 7 characters and occur more than 7 times*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#14-19teens',\n",
       " '#talkcity_adults',\n",
       " '((((((((((',\n",
       " '........',\n",
       " 'Question',\n",
       " 'actually',\n",
       " 'anything',\n",
       " 'computer',\n",
       " 'cute.-ass',\n",
       " 'everyone',\n",
       " 'football',\n",
       " 'innocent',\n",
       " 'listening',\n",
       " 'remember',\n",
       " 'seriously',\n",
       " 'something',\n",
       " 'together',\n",
       " 'tomorrow',\n",
       " 'watching']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdist5 = FreqDist(text5)\n",
    "\n",
    "sorted(w for w in set(text5) if len(w) > 7 and fdist5[w] > 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "**collocation** - sequence of words that occur together unusually often (*red wine* is a collocation, vs. *the wine* is not)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('more', 'is'), ('is', 'said'), ('said', 'than'), ('than', 'done')]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(nltk.bigrams(['more', 'is', 'said', 'than', 'done'])) # bigrams() returns a generator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*collocations are just **frequent bigrams** -- we want to focus on the cases that involve **rare words***\n",
    "\n",
    "**`collocations()`** returns bigrams that occur more often than expected, based on word frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "United States; fellow citizens; four years; years ago; Federal\n",
      "Government; General Government; American people; Vice President; Old\n",
      "World; Almighty God; Fellow citizens; Chief Magistrate; Chief Justice;\n",
      "God bless; every citizen; Indian tribes; public debt; one another;\n",
      "foreign nations; political parties\n"
     ]
    }
   ],
   "source": [
    "text4.collocations()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "would like; medium build; social drinker; quiet nights; non smoker;\n",
      "long term; age open; Would like; easy going; financially secure; fun\n",
      "times; similar interests; Age open; weekends away; poss rship; well\n",
      "presented; never married; single mum; permanent relationship; slim\n",
      "build\n"
     ]
    }
   ],
   "source": [
    "text8.collocations()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "**counting other things**\n",
    "\n",
    "*word length distribution in text1*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 4, 4, 2, 6, 8, 4, 1, 9, 1]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[len(w) for w in text1][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<FreqDist with 19 samples and 260819 outcomes>\n"
     ]
    }
   ],
   "source": [
    "fdist = FreqDist(len(w) for w in text1)\n",
    "print(fdist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({1: 47933,\n",
       "         2: 38513,\n",
       "         3: 50223,\n",
       "         4: 42345,\n",
       "         5: 26597,\n",
       "         6: 17111,\n",
       "         7: 14399,\n",
       "         8: 9966,\n",
       "         9: 6428,\n",
       "         10: 3528,\n",
       "         11: 1873,\n",
       "         12: 1053,\n",
       "         13: 567,\n",
       "         14: 177,\n",
       "         15: 70,\n",
       "         16: 22,\n",
       "         17: 12,\n",
       "         18: 1,\n",
       "         20: 1})"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(3, 50223),\n",
       " (1, 47933),\n",
       " (4, 42345),\n",
       " (2, 38513),\n",
       " (5, 26597),\n",
       " (6, 17111),\n",
       " (7, 14399),\n",
       " (8, 9966),\n",
       " (9, 6428),\n",
       " (10, 3528),\n",
       " (11, 1873),\n",
       " (12, 1053),\n",
       " (13, 567),\n",
       " (14, 177),\n",
       " (15, 70),\n",
       " (16, 22),\n",
       " (17, 12),\n",
       " (18, 1),\n",
       " (20, 1)]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdist.most_common()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdist.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50223"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdist[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19255882431878046"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdist.freq(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*words of length 3 (~50k) make up ~20% of all words in the book*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Back to Python: Making Decisions and Taking Control\n",
    "\n",
    "*skipping basic python stuff*\n",
    "\n",
    "**More accurate vocabulary size counting** -- convert all strings to lowercase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "260819"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(text1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19317"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(text1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17231"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(word.lower() for word in text1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Only include *alphabetic* words** -- no punctuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16948"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(word.lower() for word in text1 if word.isalpha()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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