nlp/edX Lightning Talk.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Generating random poems with Python #\n",
    "\n",
    "\n",
    "<div style=\"text-align:center;margin-top:40px\">(I never said they would be good poems)</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Phone autocomplete ##\n",
    "\n",
    "You can generate random text that sounds like you with your smartphone keyboard:\n",
    "\n",
    "<img align=\"left\" style=\"width:50%\" src=\"images/phone_keyboard.png\">\n",
    "<img align=\"right\" style=\"width:50%\" src=\"images/phone_autocomplete.gif\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## So, how does it work? ##\n",
    "\n",
    "First, we need a **corpus**, or the text our generator will recombine into new sentences:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "corpus = 'The quick brown fox jumps over the lazy dog'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Simplest word **tokenization** is to split on spaces:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['The', 'quick', 'brown', 'fox', 'jumps', 'over', 'the', 'lazy', 'dog']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words = corpus.split(' ')\n",
    "words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "To create **bigrams**, iterate through the list of words with two indices, one of which is offset by one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('The', 'quick'),\n",
       " ('quick', 'brown'),\n",
       " ('brown', 'fox'),\n",
       " ('fox', 'jumps'),\n",
       " ('jumps', 'over'),\n",
       " ('over', 'the'),\n",
       " ('the', 'lazy'),\n",
       " ('lazy', 'dog')]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigrams = [b for b in zip(words[:-1], words[1:])]\n",
    "bigrams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "How do we use the bigrams to predict the next word given the first word?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    " Return every second element where the first element matches the **condition**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['quick', 'lazy']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "condition = 'the'\n",
    "next_words = [bigram[1] for bigram in bigrams\n",
    "              if bigram[0].lower() == condition]\n",
    "next_words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "(<span style=\"color:blue\">The</span> <span style=\"color:red\">quick</span>) (quick brown) ... (<span style=\"color:blue\">the</span> <span style=\"color:red\">lazy</span>) (lazy dog)\n",
    "\n",
    "Either “<span style=\"color:red\">quick</span>” or “<span style=\"color:red\">lazy</span>” could be the next word."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Trigrams and Ngrams ##\n",
    "\n",
    "We can partition by threes too:\n",
    "\n",
    "(<span style=\"color:blue\">The</span> <span style=\"color:red\">quick brown</span>) (quick brown fox) ... (<span style=\"color:blue\">the</span> <span style=\"color:red\">lazy dog</span>)\n",
    "\n",
    "Or, the condition can be two words (`condition = 'the lazy'`):\n",
    "\n",
    "(The quick brown) (quick brown fox) ... (<span style=\"color:blue\">the lazy</span> <span style=\"color:red\">dog</span>)\n",
    "\n",
    "These are **trigrams**.\n",
    "\n",
    "We can partition any **N** number of words together as **ngrams**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "So earlier we got:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['quick', 'lazy']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next_words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "How do we know which one to pick as the next word?\n",
    "\n",
    "Why not the word that occurred the most often after the condition in the corpus?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "We can use a **Conditional Frequency Distribution (CFD)** to figure that out!\n",
    "\n",
    "A **CFD** can tell us: given a **condition**, what is **likely** to follow?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Conditional Frequency Distributions (CFDs) ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['The', 'quick', 'brown', 'fox', 'jumped', 'over', 'the', 'lazy', 'dog', 'and', 'the', 'quick', 'cat']\n"
     ]
    }
   ],
   "source": [
    "words = ('The quick brown fox jumped over the '\n",
    "        'lazy dog and the quick cat').split(' ')\n",
    "print(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "cfd = defaultdict(lambda: defaultdict(lambda: 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Conditional Frequency Distributions (CFDs) ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'and': {'the': 1},\n",
       " 'brown': {'fox': 1},\n",
       " 'dog': {'and': 1},\n",
       " 'fox': {'jumped': 1},\n",
       " 'jumped': {'over': 1},\n",
       " 'lazy': {'dog': 1},\n",
       " 'over': {'the': 1},\n",
       " 'quick': {'brown': 1},\n",
       " 'the': {'lazy': 1, 'quick': 2}}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(len(words) - 2):  # loop to the next-to-last word\n",
    "    cfd[words[i].lower()][words[i+1].lower()] += 1\n",
    "\n",
    "# pretty print the defaultdict\n",
    "{k: dict(v) for k, v in dict(cfd).items()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Conditional Frequency Distributions (CFDs) ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "So, what's the most likely word to follow `'the'`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'quick'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(cfd['the'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Whole sentences can be the conditions and values too ##\n",
    "\n",
    "Which is basically the way cleverbot works ([http://www.cleverbot.com/](http://www.cleverbot.com/)):\n",
    "\n",
    "![Cleverbot](images/cleverbot.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Random text! ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "her reserve and concealment towards some feelings in moving slowly together . You will shew\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "import random\n",
    "\n",
    "TEXT = nltk.corpus.gutenberg.words('austen-emma.txt')\n",
    "\n",
    "# NLTK shortcuts :)\n",
    "bigrams = nltk.bigrams(TEXT)\n",
    "cfd = nltk.ConditionalFreqDist(bigrams)\n",
    "\n",
    "# pick a random word from the corpus to start with\n",
    "word = random.choice(TEXT)\n",
    "# generate 15 more words\n",
    "for i in range(15):\n",
    "    print(word + ' ', end='')\n",
    "    if word in cfd:\n",
    "        word = random.choice(list(cfd[word].keys()))\n",
    "    else:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Random poems ##\n",
    "\n",
    "Generating random poems is accomplished by limiting the choice of the next word by some constraint:\n",
    "\n",
    "* words that rhyme with the previous line\n",
    "* words that match a certain syllable count\n",
    "* words that alliterate with words on the same line\n",
    "* etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Rhyming\n",
    "\n",
    "**Written English != Spoken English**\n",
    "\n",
    "English has a highly **nonphonemic orthography**, meaning that the letters often have no correspondence to the pronunciation. E.g.:\n",
    "\n",
    "\n",
    "\"meet\" vs. \"meat\"\n",
    "\n",
    "The vowels are spelled differently, yet they rhyme."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Fun fact: They used to be pronounced differently in Middle English during the invention of the printing press and standardized spelling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# International Phonetic Alphabet (IPA)\n",
    "\n",
    "An alphabet that can represent all varieties of human pronunciation.\n",
    "\n",
    "* meet: /mit/\n",
    "* meat: /mit/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Note: this is only the IPA transcription for only one **accent** of English."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Syllables\n",
    "\n",
    "* \"poet\" = 2 syllables\n",
    "* \"does\" = 1 syllable\n",
    "\n",
    "Can the IPA tell us the number of syllables in a word too?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Syllables\n",
    "\n",
    "* poet: /ˈpoʊət/\n",
    "* does: /ˈdʌz/\n",
    "\n",
    "Not really... We cannot easily identify three syllables from that transcription.\n",
    "\n",
    "Sometimes the transcriber denotes syllable breaks (with a `.` or a `'`), but sometimes they don't."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Arpabet\n",
    "\n",
    "A phonetic alphabet developed by ARPA in the 70s that:\n",
    "\n",
    "* Encodes phonemes specific to American English.\n",
    "* Meant to be a machine readable code. It is ASCII only.\n",
    "* Denotes how stressed every vowel is from 0-2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "This is perfect! Word's syllable count equals the number of digits in the Arpabet encoding."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# CMU Pronouncing Dictionary (CMUdict)\n",
    "\n",
    "A large open source dictionary of English words to North American pronunciations in Arpanet encoding."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Conveniently, it is also in NLTK..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Counting Syllables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import string\n",
    "from nltk.corpus import cmudict\n",
    "cmu = cmudict.dict()\n",
    "\n",
    "def count_syllables(word):\n",
    "    lower_word = word.lower()\n",
    "    if lower_word in cmu:\n",
    "        return max([len([y for y in x if y[-1] in string.digits])\n",
    "                    for x in cmu[lower_word]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "poet: 2\n",
      "does: 1\n"
     ]
    }
   ],
   "source": [
    "print(\"poet: {}\\ndoes: {}\".format(count_syllables(\"poet\"),\n",
    "                                  count_syllables(\"does\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![Buzzfeed Haiku Generator](images/buzzfeed.png)\n",
    "\n",
    "[http://mule.hallada.net/nlp/buzzfeed-haiku-generator/](http://mule.hallada.net/nlp/buzzfeed-haiku-generator/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Remember these? ##\n",
    "\n",
    "![madlibs](images/madlibs.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Mad Libs ##\n",
    "\n",
    "These worked so well because they forced the random words (chosen by you) to fit into the syntactical structure and parts-of-speech of an existing sentence.\n",
    "\n",
    "You end up with **syntactically** correct sentences that are **semantically** random.\n",
    "\n",
    "We can do the same thing!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## NLTK Syntax Trees! ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(S\n",
      "  (NP (DT the) (NN quick))\n",
      "  (VP\n",
      "    (VB brown)\n",
      "    (NP\n",
      "      (NP (JJ fox) (NN jumps))\n",
      "      (PP (IN over) (NP (DT the) (JJ lazy) (NN dog)))))\n",
      "  (. .))\n"
     ]
    }
   ],
   "source": [
    "from stat_parser import Parser\n",
    "parsed = Parser().parse('The quick brown fox jumps over the lazy dog.')\n",
    "print(parsed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## NLTK Syntax Trees! ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              S                            \n",
      "      ________________________|__________________________   \n",
      "     |               VP                                  | \n",
      "     |           ____|_____________                      |  \n",
      "     |          |                  NP                    | \n",
      "     |          |         _________|________             |  \n",
      "     |          |        |                  PP           | \n",
      "     |          |        |          ________|___         |  \n",
      "     NP         |        NP        |            NP       | \n",
      "  ___|____      |     ___|____     |     _______|____    |  \n",
      " DT       NN    VB   JJ       NN   IN   DT      JJ   NN  . \n",
      " |        |     |    |        |    |    |       |    |   |  \n",
      "the     quick brown fox     jumps over the     lazy dog  . \n",
      "\n"
     ]
    }
   ],
   "source": [
    "parsed.pretty_print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Swapping matching syntax subtrees between two corpora ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(SBARQ\n",
      "  (SQ\n",
      "    (NP (PRP I))\n",
      "    (VP (VBP do) (RB not) (VB advise) (NP (DT the) (NN custard))))\n",
      "  (. .))\n",
      "I do not advise the custard .\n",
      "==============================\n",
      "I do n't want the drone !\n",
      "(SBARQ\n",
      "  (SQ\n",
      "    (NP (PRP I))\n",
      "    (VP (VBP do) (RB n't) (VB want) (NP (DT the) (NN drone))))\n",
      "  (. !))\n"
     ]
    }
   ],
   "source": [
    "from syntax_aware_generate import generate\n",
    "\n",
    "# inserts matching syntax subtrees from trump.txt into\n",
    "# trees from austen-emma.txt\n",
    "generate('trump.txt', word_limit=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## spaCy ##\n",
    "\n",
    "![spaCy speed comparison](images/spacy_speed.png)\n",
    "\n",
    "[https://spacy.io/docs/api/#speed-comparison](https://spacy.io/docs/api/#speed-comparison)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "slide"
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   },
   "source": [
    "![Screenshot of displaCy, a dependency visualizer for spaCy](images/displacy.png)\n",
    "[https://demos.explosion.ai/displacy/](https://demos.explosion.ai/displacy/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Character-based Recurrent Neural Networks ##\n",
    "\n",
    "![RNN Paper](images/rnn_paper.png)\n",
    "\n",
    "[http://www.cs.utoronto.ca/~ilya/pubs/2011/LANG-RNN.pdf](http://www.cs.utoronto.ca/~ilya/pubs/2011/LANG-RNN.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Implementation: char-rnn ##\n",
    "\n",
    "![char-rnn](images/char-rnn.png)\n",
    "\n",
    "[https://github.com/karpathy/char-rnn](https://github.com/karpathy/char-rnn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Generating Shakespeare with char-rnn ##\n",
    "\n",
    "![Shakespeare](images/shakespeare.png)\n",
    "\n",
    "[http://karpathy.github.io/2015/05/21/rnn-effectiveness/](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![Screenshot of word-rnn readme on Github](images/word-rnn.png)\n",
    "[word-rnn](https://github.com/larspars/word-rnn)\n",
    "\n",
    "[word-rnn-tensorflow](https://github.com/hunkim/word-rnn-tensorflow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
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   },
   "source": [
    "# The end #\n",
    "\n",
    "Questions?\n",
    "\n",
    "Full write up at: [hallada.net/blog](http://www.hallada.net/2017/07/11/generating-random-poems-with-python.html)"
   ]
  }
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