notebooks/Python NLTK - Texts and Frequencies.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python NLTK: Texts and Frequencies"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(C) 2017-2024 by [Damir Cavar](http:https://damir.cavar.me/) <<dcavar@iu.edu>>**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Version:** 1.1, January 2024"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Download:** This and various other Jupyter notebooks are available from my [GitHub repo](https://github.com/dcavar/python-tutorial-notebooks)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**License:** [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/) ([CA BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Prerequisites:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -U nltk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a brief introduction to NLTK for simple frequency analysis of texts. I created this notebook for intro to corpus linguistics and natural language processing classes at Indiana University between 2017 and 2020."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this to work, in the folder with the notebook we expect a subfolder data that contains a file HOPG.txt. This file contains the novel \"A House of Pomegranates\" by Oscar Wilde taken as raw text from [Project Gutenberg](https://www.gutenberg.org/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple File Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading a text into memory in Python is faily simple. We open a file, read from it, and close the file again. The following code prints out the first 300 characters of the text in memory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A HOUSE OF POMEGRANATES\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Contents:\n",
      "\n",
      "The Young King\n",
      "The Birthday of the Infanta\n",
      "The Fisherman and his Soul\n",
      "The Star-child\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "THE YOUNG KING\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "[TO MARGARET LADY BROOKE--THE RANEE OF SARAWAK]\n",
      "\n",
      "\n",
      "It was the night before the day fixed for his coronation, and the\n",
      "young King was sitting alone in his b ...\n"
     ]
    }
   ],
   "source": [
    "ifile = open(\"data/HOPG.txt\", mode='r', encoding='utf-8')\n",
    "text = ifile.read()\n",
    "ifile.close()\n",
    "print(text[:300], \"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The optional parameters in the *open* function above define the **mode** of operations on the file and the **encoding** of the content. For example, setting the **mode** to **r** declares that *reading* from the file is the only permitted operation that we will perform in the following code. Setting the **encoding** to **utf-8** declares that all characters will be encoded using the [Unicode](https://en.wikipedia.org/wiki/Unicode) encoding schema [UTF-8](https://en.wikipedia.org/wiki/UTF-8) for the content of the file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now import the [NLTK](https://www.nltk.org/) module in Python to work with frequency profiles and [n-grams](https://en.wikipedia.org/wiki/N-gram) using the tokens or words in the text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now lower the text, which means normalizing it to all characters lower case:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a house of pomegranates\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "contents:\n",
      "\n",
      "the young king\n",
      "the birthday of the infanta\n",
      "the fisherman and his soul\n",
      "the star-child\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "the young king\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "[to margaret lady brooke--the ranee of sarawak]\n",
      "\n",
      "\n",
      "it was the night before the day fixed for his coronation, and the\n",
      "young king was sitting alone in his b ...\n"
     ]
    }
   ],
   "source": [
    "text = text.lower()\n",
    "print(text[:300], \"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To generate a frequency profile from the text file, we can use the [NLTK](https://www.nltk.org/) function *FreqDist*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "myFD = nltk.FreqDist(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<FreqDist with 39 samples and 174018 outcomes>\n"
     ]
    }
   ],
   "source": [
    "print(myFD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can remove certain characters from the distribution, or alternatively replace these characters in the text variable. The following loop removes them from the frequency profile in myFD, which is a dictionary data structure in Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for x in \":,.-[];!'\\\"\\t\\n/ ?\":\n",
    "    del myFD[x]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can print out the frequency profile by looping through the returned data structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e 17372\n",
      "t 12521\n",
      "a 11231\n",
      "h 10802\n",
      "o 9408\n",
      "n 8843\n",
      "i 8307\n",
      "s 8093\n",
      "r 7603\n",
      "d 7249\n",
      "l 5270\n",
      "w 3665\n",
      "m 3271\n",
      "u 3269\n",
      "f 3089\n",
      "c 2693\n",
      "g 2666\n",
      "y 2168\n",
      "p 1884\n",
      "b 1812\n",
      "v 1122\n",
      "k 1026\n",
      "j 103\n",
      "q 81\n",
      "z 61\n",
      "x 48\n"
     ]
    }
   ],
   "source": [
    "for x in myFD:\n",
    "    print(x, myFD[x])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To relativize the frequencies, we need to compute the total number of characters. This is assuming that we removed all punctuation symbols. The frequency distribution instance myFD provides a method to access the values associated with the individual characters. This will return a list of values, that is the frequencies associated with the characters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values([11231, 10802, 9408, 3269, 8093, 17372, 3089, 1884, 3271, 2666, 7603, 8843, 12521, 2693, 2168, 1026, 8307, 1812, 7249, 5270, 3665, 48, 1122, 81, 103, 61])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myFD.values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *sum* function can summarize these values in its list argument:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "133657"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(myFD.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To avoid type problems when we compute the relative frequency of characters, we can convert the total number of characters into a *float*. This will guarantee that the division in the following relativisation step will be a *float* as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "133657.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float(sum(myFD.values()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We store the resulting number of characters in the *total* variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "133657.0\n"
     ]
    }
   ],
   "source": [
    "total = float(sum(myFD.values()))\n",
    "print(total)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now generate a probability distribution over characters. To convert the frequencies into relative frequencies we use list comprehension and divide every single list element by total. The resulting relative frequencies are stored in the variable *relfreq*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.08402852076584093, 0.0808188123330615, 0.07038913038598801, 0.024458127894536014, 0.06055051362816762, 0.1299744869329702, 0.02311139708357961, 0.014095782488010355, 0.024473091570213306, 0.019946579677832064, 0.056884413087230745, 0.06616189200715264, 0.09368009157769515, 0.020148589299475522, 0.016220624434186013, 0.007676365622451499, 0.06215162692563801, 0.013557090163627793, 0.05423584249234982, 0.03942928540966803, 0.0274209356786401, 0.0003591282162550409, 0.008394622054961581, 0.0006060288649303815, 0.0007706292973806085, 0.0004563921081574478]\n"
     ]
    }
   ],
   "source": [
    "relfrq = [ x/total for x in myFD.values() ]\n",
    "print(relfrq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us compute the [Entropy](https://en.wikipedia.org/wiki/Entropy_(information_theory)) for the character distribution using the relative frequencies. We will need the [logarithm](https://en.wikipedia.org/wiki/Logarithm) function from the Python *math* module for that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from math import log"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can define the [Entropy](https://en.wikipedia.org/wiki/Entropy_(information_theory)) function according to the equation $I = - \\sum P(x) log_2( P(x) )$ as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def entropy(p):\n",
    "    return -sum( [ x * log(x, 2) for x in p ] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.75"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropy([1/8, 1/16, 1/4, 1/8, 1/16, 1/16, 1/4, 1/16])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropy([1/8, 1/8, 1/8, 1/8, 1/8, 1/8, 1/8, 1/8])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now compute the entropy of the character distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.700439718141092\n",
      "4.124824125135032\n"
     ]
    }
   ],
   "source": [
    "print(entropy([ 1/len(relfrq) ] * len(relfrq)))\n",
    "print(entropy(relfrq))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We might be interested in the point-wise entropy of the characters in this distribution, thus needing the entropy of each single character. We can compute that in the following way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.3002319806578157, 0.29330480822209687, 0.2694850339615855, 0.13093762006835424, 0.24497024185626542, 0.38260584969385675, 0.12561626066788154, 0.08666922421352652, 0.13099613411450642, 0.11265259339509692, 0.2352638524022937, 0.2592127456210649, 0.32002184459468397, 0.11350057702872672, 0.09644826809662171, 0.053929238563860095, 0.24910770047189124, 0.08411915007238721, 0.2280405439219216, 0.18392139623527803, 0.1422756742696596, 0.004109580806277946, 0.05789199083219161, 0.006477433994507777, 0.007969598004767611, 0.005064783367910896]\n"
     ]
    }
   ],
   "source": [
    "entdist = [ -x * log(x, 2) for x in relfrq ]\n",
    "print(entdist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could now compute the variance over this point-wise entropy distribution or other properties of the frequency distribution as for example median, mode, or standard deviation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## From Characters to Words/Tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the frequency profile is for the characters in the text, not the words or tokens. In order to generate a frequency profile over words/tokens in the text, we need to utilize a **tokenizer**. [NLTK](https://www.nltk.org/) provides basic tokenization functions. We will use the *word_tokenize* function to generate a list of tokens:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "tokens = nltk.word_tokenize(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can print out the first 20 tokens to verify our data structure is a list with lower-case strings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a',\n",
       " 'house',\n",
       " 'of',\n",
       " 'pomegranates',\n",
       " 'contents',\n",
       " ':',\n",
       " 'the',\n",
       " 'young',\n",
       " 'king',\n",
       " 'the',\n",
       " 'birthday',\n",
       " 'of',\n",
       " 'the',\n",
       " 'infanta',\n",
       " 'the',\n",
       " 'fisherman',\n",
       " 'and',\n",
       " 'his',\n",
       " 'soul',\n",
       " 'the']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokens[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now generate a frequency profile from the token list, as we did with the characters above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<FreqDist with 4009 samples and 38127 outcomes>\n"
     ]
    }
   ],
   "source": [
    "myTokenFD = nltk.FreqDist(tokens)\n",
    "print(myTokenFD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The frequency profile can be printed out in the same way as above by looping over the tokens and their frequencies. Note that we restrict the loop to the first 20 tokens here just to keep the notebook smaller. You can remove the [:20] selector in your own experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(('a', 673), ('house', 26), ('of', 1108), ('pomegranates', 6), ('contents', 1), (':', 33), ('the', 2552), ('young', 112), ('king', 70), ('birthday', 8))\n"
     ]
    }
   ],
   "source": [
    "print(tuple(myTokenFD.items())[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a 673\n",
      "house 26\n",
      "of 1108\n",
      "pomegranates 6\n",
      "contents 1\n",
      ": 33\n",
      "the 2552\n",
      "young 112\n",
      "king 70\n",
      "birthday 8\n",
      "infanta 37\n",
      "fisherman 76\n",
      "and 2118\n",
      "his 483\n",
      "soul 99\n",
      "star-child 42\n",
      "[ 4\n",
      "to 697\n",
      "margaret 1\n",
      "lady 4\n",
      "brooke 1\n",
      "-- 26\n",
      "ranee 1\n",
      "sarawak 1\n",
      "] 4\n",
      "it 353\n",
      "was 356\n",
      "night 14\n",
      "before 53\n",
      "day 46\n",
      "fixed 2\n",
      "for 267\n",
      "coronation 4\n",
      ", 2675\n",
      "sitting 2\n",
      "alone 10\n",
      "in 472\n",
      "beautiful 25\n",
      "chamber 10\n",
      ". 1341\n",
      "courtiers 3\n",
      "had 235\n",
      "all 82\n",
      "taken 5\n",
      "their 176\n",
      "leave 10\n",
      "him 372\n",
      "bowing 2\n",
      "heads 7\n",
      "ground 16\n",
      "according 1\n",
      "ceremonious 1\n",
      "usage 1\n",
      "retired 3\n",
      "great 83\n",
      "hall 4\n",
      "palace 25\n",
      "receive 3\n",
      "few 9\n",
      "last 14\n",
      "lessons 1\n",
      "from 184\n",
      "professor 1\n",
      "etiquette 2\n",
      "; 60\n",
      "there 97\n",
      "being 11\n",
      "some 52\n",
      "them 154\n",
      "who 132\n",
      "still 14\n",
      "quite 21\n",
      "natural 4\n",
      "manners 1\n",
      "which 59\n",
      "courtier 1\n",
      "is 242\n",
      "i 376\n",
      "need 13\n",
      "hardly 4\n",
      "say 11\n",
      "very 16\n",
      "grave 7\n",
      "offence 1\n",
      "lad 5\n",
      "he 645\n",
      "only 30\n",
      "but 212\n",
      "sixteen 1\n",
      "years 16\n",
      "age 4\n",
      "not 208\n",
      "sorry 1\n",
      "at 203\n",
      "departure 1\n",
      "flung 12\n",
      "himself 42\n",
      "back 42\n",
      "with 391\n",
      "deep 13\n",
      "sigh 1\n",
      "relief 1\n",
      "on 209\n",
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      "melons 2\n",
      "musk 1\n",
      "topazes 1\n",
      "citrons 1\n",
      "rose-apples 1\n",
      "red-gold 1\n",
      "oranges 2\n",
      "oval 3\n",
      "elephant 1\n",
      "trunk 1\n",
      "vermilion 2\n",
      "turmeric 1\n",
      "eating 1\n",
      "bird-sellers 1\n",
      "caged 1\n",
      "scourge 1\n",
      "'one 1\n",
      "palanquin 2\n",
      "poles 1\n",
      "muslin 1\n",
      "beetles 1\n",
      "pale-faced 1\n",
      "circassian 1\n",
      "curiosity 1\n",
      "square 2\n",
      "tomb 1\n",
      "hammer 1\n",
      "armenian 1\n",
      "caftan 1\n",
      "spread 2\n",
      "mosque 1\n",
      "beard 2\n",
      "dyed 3\n",
      "rose-leaves 1\n",
      "palms 1\n",
      "saffron 1\n",
      "stall 1\n",
      "eyebrows 1\n",
      "marvelled 1\n",
      "boldness 1\n",
      "counselled 1\n",
      "flee 3\n",
      "sellers 2\n",
      "abominated 1\n",
      "tea-house 1\n",
      "inside 2\n",
      "arcade 1\n",
      "alabaster 1\n",
      "tiles 1\n",
      "pillars 2\n",
      "peach-blossom 1\n",
      "veiled 3\n",
      "hastened 2\n",
      "butts 1\n",
      "lances 1\n",
      "rang 1\n",
      "watered 1\n",
      "terraces 1\n",
      "planted 1\n",
      "tulip-cups 1\n",
      "moonflowers 1\n",
      "silver-studded 1\n",
      "aloes 1\n",
      "cypress-trees 1\n",
      "burnt-out 1\n",
      "torches 2\n",
      "approached 1\n",
      "eunuchs 3\n",
      "fat 1\n",
      "swayed 1\n",
      "yellow-lidded 1\n",
      "captain 4\n",
      "guard 5\n",
      "munching 1\n",
      "pastilles 1\n",
      "gesture 1\n",
      "dismissed 1\n",
      "plucking 1\n",
      "mulberries 1\n",
      "elder 1\n",
      "motioned 1\n",
      "drawing 1\n",
      "gerfalcon 1\n",
      "perched 1\n",
      "wrist 1\n",
      "brass- 1\n",
      "turbaned 1\n",
      "nubian 3\n",
      "mighty 1\n",
      "scimitar 2\n",
      "blade 1\n",
      "whizzed 1\n",
      "hurt 3\n",
      "lance 1\n",
      "flight 1\n",
      "shaft 1\n",
      "mid-air 1\n",
      "dishonour 1\n",
      "writhed 1\n",
      "snake 1\n",
      "bubbled 1\n",
      "wiped 1\n",
      "sweat 1\n",
      "napkin 1\n",
      "purfled 1\n",
      "half 5\n",
      "wondered 1\n",
      "eight 1\n",
      "brass-sealed 1\n",
      "lamps 1\n",
      "wine-jars 1\n",
      "brim 1\n",
      "spoken 3\n",
      "granite 1\n",
      "swung 1\n",
      "dazzled 1\n",
      "couldst 1\n",
      "believe 2\n",
      "tortoise-shells 1\n",
      "size 1\n",
      "piled 1\n",
      "stored 2\n",
      "elephant-hide 1\n",
      "gold-dust 1\n",
      "bottles 1\n",
      "opals 1\n",
      "sapphires 1\n",
      "former 1\n",
      "latter 1\n",
      "ranged 1\n",
      "bags 1\n",
      "turquoise-stones 1\n",
      "heaped 1\n",
      "amethysts 1\n",
      "chalcedonies 1\n",
      "sards 1\n",
      "cedar 1\n",
      "lynx-stones 1\n",
      "carbuncles 1\n",
      "both 1\n",
      "wine-coloured 1\n",
      "tithe 1\n",
      "promised 1\n",
      "drivers 1\n",
      "share 2\n",
      "wearest 1\n",
      "nay 1\n",
      "leaden 1\n",
      "riches 5\n",
      "waits 1\n",
      "world's 1\n",
      "inn 3\n",
      "standeth 1\n",
      "different-coloured 1\n",
      "wines 1\n",
      "ate 2\n",
      "barley 1\n",
      "served 1\n",
      "vinegar 1\n",
      "laid 4\n",
      "quill 1\n",
      "pigeons 1\n",
      "'let 2\n",
      "hence 7\n",
      "sea-gods 1\n",
      "jealous 1\n",
      "monsters 1\n",
      "haste 1\n",
      "didst 11\n",
      "nevertheless 2\n",
      "jewellers 1\n",
      "booth 1\n",
      "hurriedly 2\n",
      "league 3\n",
      "jar 1\n",
      "'smite 1\n",
      "smote 1\n",
      "smite 1\n",
      "nowhere 3\n",
      "rested 1\n",
      "merchant 12\n",
      "corded 1\n",
      "kinsman 1\n",
      "kinsmen 1\n",
      "guest-chamber 1\n",
      "quench 2\n",
      "thirst 1\n",
      "rice 1\n",
      "guest- 1\n",
      "goat's-hair 1\n",
      "covering 1\n",
      "lamb's- 1\n",
      "waked 1\n",
      "'rise 3\n",
      "sleepeth 1\n",
      "tray 1\n",
      "purses 4\n",
      "awoke 1\n",
      "'dost 1\n",
      "shedding 1\n",
      "kindness 2\n",
      "'strike 1\n",
      "swooned 1\n",
      "hate 2\n",
      "gavest 3\n",
      "thyself 3\n",
      "forget 1\n",
      "tempted 5\n",
      "ways 1\n",
      "anywhere 4\n",
      "strove 1\n",
      "stirred 4\n",
      "avails 1\n",
      "mayest 2\n",
      "receiveth 1\n",
      "punishment 2\n",
      "reward 2\n",
      "'she 1\n",
      "worships 1\n",
      "abide 2\n",
      "bind 2\n",
      "dancing-girls 1\n",
      "samaris 1\n",
      "henna 1\n",
      "pleasant 1\n",
      "eater 1\n",
      "tulip-trees 1\n",
      "tails 1\n",
      "disks 2\n",
      "feeds 1\n",
      "stibium 1\n",
      "swallow 1\n",
      "hook 1\n",
      "hangs 1\n",
      "laughs 1\n",
      "ankles 1\n",
      "tight 1\n",
      "bound 3\n",
      "wickedness 1\n",
      "power 3\n",
      "loosed 1\n",
      "pours 1\n",
      "givest 1\n",
      "wattles 1\n",
      "abode 1\n",
      "space 4\n",
      "pools 1\n",
      "tide 1\n",
      "prevail 2\n",
      "deaf 1\n",
      "hearken 1\n",
      "escapes 1\n",
      "widows 2\n",
      "fens 1\n",
      "wallets 1\n",
      "mend 1\n",
      "store 2\n",
      "rivers 1\n",
      "compassed 1\n",
      "hurrying 1\n",
      "received 1\n",
      "smitten 1\n",
      "toyed 1\n",
      "tasted 1\n",
      "confession 1\n",
      "shells 1\n",
      "moaned 1\n",
      "sea-king 1\n",
      "hoarsely 1\n",
      "'flee 1\n",
      "nigher 1\n",
      "tarriest 1\n",
      "greatness 1\n",
      "safety 1\n",
      "fires 1\n",
      "destroy 1\n",
      "evilly 3\n",
      "cover 1\n",
      "fulness 1\n",
      "break 2\n",
      "bless 2\n",
      "musicians 2\n",
      "candle-bearers 2\n",
      "swingers 2\n",
      "censers 2\n",
      "sake 1\n",
      "forsook 1\n",
      "lieth 1\n",
      "judgment 1\n",
      "fullers 3\n",
      "resting 1\n",
      "deaths 1\n",
      "commanded 1\n",
      "herbs 1\n",
      "pit 1\n",
      "wrath 3\n",
      "robed 1\n",
      "understood 1\n",
      "tabernacle 1\n",
      "incensed 1\n",
      "veils 1\n",
      "sacristy 1\n",
      "deacons 1\n",
      "unrobe 1\n",
      "alb 1\n",
      "maniple 1\n",
      "unrobed 1\n",
      "whence 1\n",
      "blessed 3\n",
      "bright-eyed 1\n",
      "peer 1\n",
      "fullers' 1\n",
      "remained 1\n",
      "margot 1\n",
      "tennant 1\n",
      "asquith 1\n",
      "woodcutters 2\n",
      "pine-forest 1\n",
      "snow 7\n",
      "frost 1\n",
      "snapping 1\n",
      "twigs 1\n",
      "mountain- 1\n",
      "torrent 1\n",
      "motionless 1\n",
      "ice-king 1\n",
      "animals 2\n",
      "'ugh 1\n",
      "wolf 3\n",
      "limped 1\n",
      "brushwood 1\n",
      "perfectly 1\n",
      "monstrous 1\n",
      "government 2\n",
      "'weet 1\n",
      "weet 2\n",
      "twittered 1\n",
      "linnets 1\n",
      "shroud 1\n",
      "bridal 1\n",
      "turtle-doves 1\n",
      "frost-bitten 1\n",
      "duty 1\n",
      "romantic 1\n",
      "view 1\n",
      "situation 1\n",
      "'nonsense 1\n",
      "growled 1\n",
      "fault 1\n",
      "thoroughly 1\n",
      "practical 1\n",
      "loss 1\n",
      "argument 1\n",
      "'well 1\n",
      "woodpecker 1\n",
      "philosopher 1\n",
      "atomic 1\n",
      "theory 1\n",
      "explanations 1\n",
      "fir-tree 2\n",
      "rubbing 1\n",
      "holes 1\n",
      "venture 1\n",
      "enjoy 1\n",
      "owls 1\n",
      "rime 1\n",
      "rolled 1\n",
      "'tu-whit 1\n",
      "tu-whoo 2\n",
      "tu-whit 1\n",
      "blowing 1\n",
      "lustily 1\n",
      "stamping 1\n",
      "iron-shod 1\n",
      "boots 2\n",
      "caked 1\n",
      "drift 1\n",
      "millers 1\n",
      "grinding 1\n",
      "marsh-water 1\n",
      "faggots 2\n",
      "bundles 1\n",
      "pick 1\n",
      "trust 1\n",
      "saint 1\n",
      "martin 1\n",
      "watches 1\n",
      "retraced 1\n",
      "warily 1\n",
      "village 7\n",
      "overjoyed 1\n",
      "deliverance 1\n",
      "beast 2\n",
      "'truly 2\n",
      "'much 1\n",
      "injustice 1\n",
      "parcelled 1\n",
      "equal 1\n",
      "division 1\n",
      "bewailing 1\n",
      "sky 2\n",
      "passing 2\n",
      "clump 1\n",
      "willow-trees 1\n",
      "sheepfold 1\n",
      "stone's-throw 1\n",
      "crook 1\n",
      "whoever 1\n",
      "finds 1\n",
      "mate 1\n",
      "outstripped 1\n",
      "forced 1\n",
      "willows 1\n",
      "stooping 1\n",
      "folds 2\n",
      "comrade 4\n",
      "divide 1\n",
      "alas 1\n",
      "hope 1\n",
      "perish 1\n",
      "pot 1\n",
      "tenderly 1\n",
      "shield 3\n",
      "marvelling 1\n",
      "foolishness 1\n",
      "softness 1\n",
      "godspeed 1\n",
      "husband 1\n",
      "safe 1\n",
      "bundle 1\n",
      "threshold 2\n",
      "'show 1\n",
      "goodman 1\n",
      "'have 1\n",
      "changeling 1\n",
      "bad 1\n",
      "tend 1\n",
      "finding 1\n",
      "appeased 1\n",
      "careth 2\n",
      "giveth 1\n",
      "sparrows 2\n",
      "feedeth 1\n",
      "'do 1\n",
      "tremble 1\n",
      "shivered 1\n",
      "'into 1\n",
      "closer 1\n",
      "morrow 4\n",
      "woodcutter 13\n",
      "black-haired 1\n",
      "sawn 1\n",
      "daffodil 1\n",
      "pure 1\n",
      "mower 1\n",
      "selfish 1\n",
      "despised 1\n",
      "parentage 1\n",
      "maimed 1\n",
      "afflicted 1\n",
      "highway 1\n",
      "beg 1\n",
      "elsewhere 1\n",
      "outlaws 1\n",
      "alms 1\n",
      "weakly 1\n",
      "ill-favoured 2\n",
      "summer 1\n",
      "winds 1\n",
      "fairness 1\n",
      "chide 1\n",
      "dealest 1\n",
      "desolate 1\n",
      "succour 1\n",
      "teach 1\n",
      "living 1\n",
      "roam 1\n",
      "snare 1\n",
      "blind-worm 1\n",
      "mole 4\n",
      "frown 1\n",
      "flout 1\n",
      "fleet 1\n",
      "ruled 4\n",
      "beggar-woman 9\n",
      "garments 1\n",
      "torn 1\n",
      "travelled 1\n",
      "plight 1\n",
      "chestnut-tree 1\n",
      "'see 1\n",
      "sitteth 1\n",
      "green-leaved 1\n",
      "ill- 1\n",
      "favoured 1\n",
      "gaze 1\n",
      "cleaving 1\n",
      "rebuked 1\n",
      "treat 1\n",
      "truly 1\n",
      "swoon 2\n",
      "meat 1\n",
      "comfort 1\n",
      "'didst 1\n",
      "ten 2\n",
      "'yea 1\n",
      "'bare 1\n",
      "star- 3\n",
      "scornfully 1\n",
      "recognised 2\n",
      "tellest 1\n",
      "playmates 1\n",
      "drave 3\n",
      "sealed 1\n",
      "comeliness 2\n",
      "forgiveness 5\n",
      "sorely 1\n",
      "inquiry 2\n",
      "perchance 1\n",
      "blinded 1\n",
      "linnet 2\n",
      "clipt 1\n",
      "squirrel 2\n",
      "carlots 1\n",
      "byres 1\n",
      "mildew 1\n",
      "hired 1\n",
      "flints 1\n",
      "bleed 1\n",
      "overtake 1\n",
      "deny 1\n",
      "sport 1\n",
      "loving-kindness 2\n",
      "charity 2\n",
      "pride 2\n",
      "strong-walled 1\n",
      "footsore 1\n",
      "dropped 1\n",
      "roughly 1\n",
      "ye 4\n",
      "wagged 1\n",
      "sees 1\n",
      "marsh 1\n",
      "crawls 1\n",
      "fen 1\n",
      "dwells 1\n",
      "banner 1\n",
      "tarrieth 1\n",
      "pricked 1\n",
      "helmet 2\n",
      "evil-visaged 1\n",
      "pomegranate 1\n",
      "jars 1\n",
      "scarf 3\n",
      "dungeon 3\n",
      "mouldy 1\n",
      "trencher 2\n",
      "'eat 2\n",
      "brackish 1\n",
      "'drink 2\n",
      "drunk 1\n",
      "locking 1\n",
      "fastening 1\n",
      "subtlest 1\n",
      "magicians 1\n",
      "libya 1\n",
      "giaours 1\n",
      "bringest 5\n",
      "stripes 2\n",
      "ill 1\n",
      "bought 1\n",
      "magician 11\n",
      "sweet-scented 1\n",
      "gladly 1\n",
      "briars 1\n",
      "encompassed 1\n",
      "nettles 1\n",
      "stung 1\n",
      "thistle 1\n",
      "daggers 1\n",
      "sore 2\n",
      "distress 1\n",
      "morn 1\n",
      "fate 2\n",
      "forgetting 1\n",
      "trap 2\n",
      "hunter 1\n",
      "released 1\n",
      "rendered 1\n",
      "repaid 1\n",
      "hundred-fold 1\n",
      "dealt 1\n",
      "cowl 1\n",
      "eyelets 1\n",
      "gleamed 1\n",
      "coals 1\n",
      "clattered 1\n",
      "money 4\n",
      "wallet 4\n",
      "'hast 2\n",
      "rescued 1\n",
      "'follow 1\n",
      "thank 1\n",
      "succoured 3\n",
      "loaded 1\n",
      "seekest 1\n",
      "farthest 1\n",
      "awaited 1\n",
      "concourse 1\n",
      "abased 1\n",
      "saith 1\n",
      "prophesied 1\n",
      "'mother 2\n",
      "accept 1\n",
      "humility 1\n",
      "hatred 1\n",
      "rejected 1\n",
      "'thrice 1\n",
      "sobbed 1\n",
      "suffering 2\n",
      "washed 1\n",
      "clothed 1\n",
      "banished 1\n",
      "gifts 1\n",
      "plenty 1\n",
      "testing 1\n"
     ]
    }
   ],
   "source": [
    "for token in list(myTokenFD.items()):\n",
    "    print(token[0], token[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n"
     ]
    }
   ],
   "source": [
    "stopwords = nltk.corpus.stopwords.words('english')\n",
    "print(stopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(',', '.', \"'\", 'said', 'thou', '?', 'thee', 'little', 'young', 'soul', 'came', 'great', 'answered', 'upon', 'one', 'fisherman', 'thy', 'gold', 'king', 'went')\n"
     ]
    }
   ],
   "source": [
    "for x in stopwords:\n",
    "    del myTokenFD[x]\n",
    "print(tuple(myTokenFD)[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Counting N-grams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[NLTK](https://www.nltk.org/) provides simple methods to generate [n-gram](https://en.wikipedia.org/wiki/N-gram) models or frequency profiles over [n-grams](https://en.wikipedia.org/wiki/N-gram) from any kind of list or sequence. We can for example generate a bi-gram model, that is an [n-grams](https://en.wikipedia.org/wiki/N-gram) model for n = 2, from the text tokens:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "myTokenBigrams = nltk.ngrams(tokens, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To store the bigrams in a list that we want to process and analyze further, we convert the **Python generator object** myTokenBigrams to a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "bigrams = list(myTokenBigrams)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us verify that the resulting data structure is indeed a list of string tuples. We will print out the first 20 tuples from the bigram list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('a', 'house'), ('house', 'of'), ('of', 'pomegranates'), ('pomegranates', 'contents'), ('contents', ':'), (':', 'the'), ('the', 'young'), ('young', 'king'), ('king', 'the'), ('the', 'birthday'), ('birthday', 'of'), ('of', 'the'), ('the', 'infanta'), ('infanta', 'the'), ('the', 'fisherman'), ('fisherman', 'and'), ('and', 'his'), ('his', 'soul'), ('soul', 'the'), ('the', 'star-child')]\n"
     ]
    }
   ],
   "source": [
    "print(bigrams[:20])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now verify the number of bigrams and check that there are exactly *number of tokens - 1 = number of bigrams* in the resulting list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38126\n",
      "38127\n"
     ]
    }
   ],
   "source": [
    "print(len(bigrams))\n",
    "print(len(tokens))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The frequency profile from these bigrams is generated in exactly the same way as from the token list in the examples above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<FreqDist with 17766 samples and 38126 outcomes>\n"
     ]
    }
   ],
   "source": [
    "myBigramFD = nltk.FreqDist(bigrams)\n",
    "print(myBigramFD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we would want to know some more general properties of the frequency distribution, we can print out information about it. The print statement for this bigram frequency distribution tells us that we have 17,766 types and 38,126 tokens:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<FreqDist with 17766 samples and 38126 outcomes>\n"
     ]
    }
   ],
   "source": [
    "print(myBigramFD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bigrams and their corresponding frequencies can be printed using a *for* loop. We restrict the number of printed items to 20, just to keep this list reasonably long. If you would like to see the full frequency profile, remove the [:20] restrictor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('a', 'house') 4\n",
      "('house', 'of') 5\n",
      "('of', 'pomegranates') 4\n",
      "('pomegranates', 'contents') 1\n",
      "('contents', ':') 1\n",
      "(':', 'the') 2\n",
      "('the', 'young') 107\n",
      "('young', 'king') 24\n",
      "('king', 'the') 1\n",
      "('the', 'birthday') 3\n",
      "('birthday', 'of') 3\n",
      "('of', 'the') 354\n",
      "('the', 'infanta') 33\n",
      "('infanta', 'the') 1\n",
      "('the', 'fisherman') 5\n",
      "('fisherman', 'and') 3\n",
      "('and', 'his') 53\n",
      "('his', 'soul') 51\n",
      "('soul', 'the') 1\n",
      "('the', 'star-child') 41\n",
      "...\n"
     ]
    }
   ],
   "source": [
    "for bigram in list(myBigramFD.items())[:20]:\n",
    "    print(bigram[0], bigram[1])\n",
    "print(\"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pretty printing the bigrams is possible as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a house 4\n",
      "house of 5\n",
      "of pomegranates 4\n",
      "pomegranates contents 1\n",
      "contents : 1\n",
      ": the 2\n",
      "the young 107\n",
      "young king 24\n",
      "king the 1\n",
      "the birthday 3\n",
      "birthday of 3\n",
      "of the 354\n",
      "the infanta 33\n",
      "infanta the 1\n",
      "the fisherman 5\n",
      "fisherman and 3\n",
      "and his 53\n",
      "his soul 51\n",
      "soul the 1\n",
      "the star-child 41\n",
      "star-child the 1\n",
      "king [ 1\n",
      "[ to 4\n",
      "to margaret 1\n",
      "margaret lady 1\n",
      "lady brooke 1\n",
      "brooke -- 1\n",
      "-- the 4\n",
      "the ranee 1\n",
      "ranee of 1\n",
      "of sarawak 1\n",
      "sarawak ] 1\n",
      "] it 2\n",
      "it was 52\n",
      "was the 20\n",
      "the night 3\n",
      "night before 1\n",
      "before the 8\n",
      "the day 7\n",
      "day fixed 1\n",
      "fixed for 1\n",
      "for his 7\n",
      "his coronation 2\n",
      "coronation , 3\n",
      ", and 1271\n",
      "and the 287\n",
      "king was 1\n",
      "was sitting 1\n",
      "sitting alone 1\n",
      "alone in 1\n",
      "in his 31\n",
      "his beautiful 1\n",
      "beautiful chamber 1\n",
      "chamber . 1\n",
      ". his 8\n",
      "his courtiers 1\n",
      "courtiers had 1\n",
      "had all 2\n",
      "all taken 1\n",
      "taken their 1\n",
      "their leave 1\n",
      "leave of 1\n",
      "of him 8\n",
      "him , 146\n",
      ", bowing 1\n",
      "bowing their 1\n",
      "their heads 5\n",
      "heads to 1\n",
      "to the 149\n",
      "the ground 15\n",
      "ground , 6\n",
      ", according 1\n",
      "according to 1\n",
      "the ceremonious 1\n",
      "ceremonious usage 1\n",
      "usage of 1\n",
      "day , 3\n",
      "and had 9\n",
      "had retired 2\n",
      "retired to 2\n",
      "the great 25\n",
      "great hall 2\n",
      "hall of 1\n",
      "the palace 18\n",
      "palace , 5\n",
      ", to 6\n",
      "to receive 1\n",
      "receive a 1\n",
      "a few 9\n",
      "few last 1\n",
      "last lessons 1\n",
      "lessons from 1\n",
      "from the 69\n",
      "the professor 1\n",
      "professor of 1\n",
      "of etiquette 1\n",
      "etiquette ; 1\n",
      "; there 2\n",
      "there being 1\n",
      "being some 1\n",
      "some of 10\n",
      "of them 28\n",
      "them who 1\n",
      "who had 13\n",
      "had still 1\n",
      "still quite 1\n",
      "quite natural 1\n",
      "natural manners 1\n",
      "manners , 1\n",
      ", which 15\n",
      "which in 1\n",
      "in a 46\n",
      "a courtier 1\n",
      "courtier is 1\n",
      "is , 1\n",
      ", i 20\n",
      "i need 1\n",
      "need hardly 1\n",
      "hardly say 1\n",
      "say , 1\n",
      ", a 12\n",
      "a very 2\n",
      "very grave 1\n",
      "grave offence 1\n",
      "offence . 1\n",
      ". the 112\n",
      "the lad 2\n",
      "lad -- 1\n",
      "-- for 1\n",
      "for he 8\n",
      "he was 43\n",
      "was only 4\n",
      "only a 6\n",
      "a lad 2\n",
      "lad , 1\n",
      ", being 5\n",
      "being but 2\n",
      "but sixteen 1\n",
      "sixteen years 1\n",
      "years of 5\n",
      "of age 4\n",
      "age -- 1\n",
      "-- was 1\n",
      "was not 6\n",
      "not sorry 1\n",
      "sorry at 1\n",
      "at their 5\n",
      "their departure 1\n",
      "departure , 1\n",
      "had flung 1\n",
      "flung himself 6\n",
      "himself back 1\n",
      "back with 2\n",
      "with a 43\n",
      "a deep 3\n",
      "deep sigh 1\n",
      "sigh of 1\n",
      "of relief 1\n",
      "relief on 1\n",
      "on the 80\n",
      "the soft 3\n",
      "soft cushions 1\n",
      "cushions of 1\n",
      "of his 41\n",
      "his embroidered 2\n",
      "embroidered couch 1\n",
      "couch , 3\n",
      ", lying 1\n",
      "lying there 1\n",
      "there , 4\n",
      ", wild-eyed 1\n",
      "wild-eyed and 1\n",
      "and open-mouthed 1\n",
      "open-mouthed , 1\n",
      ", like 9\n",
      "like a 17\n",
      "a brown 2\n",
      "brown woodland 1\n",
      "woodland faun 1\n",
      "faun , 1\n",
      ", or 27\n",
      "or some 2\n",
      "some young 1\n",
      "young animal 1\n",
      "animal of 1\n",
      "the forest 30\n",
      "forest newly 1\n",
      "newly snared 1\n",
      "snared by 1\n",
      "by the 36\n",
      "the hunters 2\n",
      "hunters . 1\n",
      ". and 205\n",
      "and , 18\n",
      ", indeed 6\n",
      "indeed , 9\n",
      ", it 9\n",
      "hunters who 1\n",
      "had found 2\n",
      "found him 2\n",
      ", coming 2\n",
      "coming upon 1\n",
      "upon him 7\n",
      "him almost 1\n",
      "almost by 1\n",
      "by chance 1\n",
      "chance as 1\n",
      "as , 1\n",
      ", bare-limbed 1\n",
      "bare-limbed and 1\n",
      "and pipe 2\n",
      "pipe in 1\n",
      "in hand 1\n",
      "hand , 17\n",
      ", he 73\n",
      "was following 1\n",
      "following the 1\n",
      "the flock 1\n",
      "flock of 1\n",
      "the poor 6\n",
      "poor goatherd 1\n",
      "goatherd who 1\n",
      "had brought 3\n",
      "brought him 6\n",
      "him up 3\n",
      "up , 13\n",
      "and whose 4\n",
      "whose son 1\n",
      "son he 1\n",
      "he had 63\n",
      "had always 3\n",
      "always fancied 1\n",
      "fancied himself 1\n",
      "himself to 1\n",
      "to be 17\n",
      "be . 3\n",
      "the child 15\n",
      "child of 5\n",
      "the old 13\n",
      "old king 2\n",
      "king 's 13\n",
      "'s only 2\n",
      "only daughter 2\n",
      "daughter by 1\n",
      "by a 13\n",
      "a secret 2\n",
      "secret marriage 1\n",
      "marriage with 1\n",
      "with one 2\n",
      "one much 1\n",
      "much beneath 1\n",
      "beneath her 3\n",
      "her in 8\n",
      "in station 1\n",
      "station -- 1\n",
      "-- a 2\n",
      "a stranger 1\n",
      "stranger , 1\n",
      ", some 8\n",
      "some said 2\n",
      "said , 49\n",
      ", who 30\n",
      "who , 1\n",
      ", by 2\n",
      "the wonderful 3\n",
      "wonderful magic 1\n",
      "magic of 1\n",
      "his lute-playing 1\n",
      "lute-playing , 1\n",
      ", had 10\n",
      "had made 2\n",
      "made the 4\n",
      "young princess 1\n",
      "princess love 1\n",
      "love him 1\n",
      "him ; 1\n",
      "; while 1\n",
      "while others 1\n",
      "others spoke 1\n",
      "spoke of 2\n",
      "of an 5\n",
      "an artist 1\n",
      "artist from 1\n",
      "from rimini 1\n",
      "rimini , 1\n",
      "to whom 1\n",
      "whom the 2\n",
      "the princess 2\n",
      "princess had 1\n",
      "had shown 2\n",
      "shown much 2\n",
      "much , 2\n",
      ", perhaps 2\n",
      "perhaps too 1\n",
      "too much 2\n",
      "much honour 1\n",
      "honour , 1\n",
      "and who 4\n",
      "had suddenly 1\n",
      "suddenly disappeared 1\n",
      "disappeared from 1\n",
      "the city 34\n",
      "city , 15\n",
      ", leaving 1\n",
      "leaving his 1\n",
      "his work 1\n",
      "work in 1\n",
      "in the 158\n",
      "the cathedral 5\n",
      "cathedral unfinished 1\n",
      "unfinished -- 1\n",
      "-- he 1\n",
      "had been 38\n",
      "been , 2\n",
      ", when 15\n",
      "when but 1\n",
      "but a 14\n",
      "a week 1\n",
      "week old 1\n",
      "old , 3\n",
      ", stolen 1\n",
      "stolen away 1\n",
      "away from 8\n",
      "from his 28\n",
      "his mother 6\n",
      "mother 's 2\n",
      "'s side 1\n",
      "side , 7\n",
      ", as 29\n",
      "as she 13\n",
      "she slept 1\n",
      "slept , 1\n",
      "and given 1\n",
      "given into 1\n",
      "into the 55\n",
      "the charge 1\n",
      "charge of 1\n",
      "of a 48\n",
      "a common 1\n",
      "common peasant 1\n",
      "peasant and 1\n",
      "his wife 7\n",
      "wife , 1\n",
      "who were 5\n",
      "were without 1\n",
      "without children 1\n",
      "children of 10\n",
      "of their 15\n",
      "their own 2\n",
      "own , 4\n",
      "and lived 1\n",
      "lived in 3\n",
      "a remote 2\n",
      "remote part 2\n",
      "part of 6\n",
      "forest , 17\n",
      ", more 1\n",
      "more than 4\n",
      "than a 3\n",
      "a day 9\n",
      "day 's 6\n",
      "'s ride 1\n",
      "ride from 1\n",
      "the town 4\n",
      "town . 1\n",
      ". grief 1\n",
      "grief , 2\n",
      "or the 6\n",
      "the plague 2\n",
      "plague , 1\n",
      "as the 20\n",
      "the court 4\n",
      "court physician 1\n",
      "physician stated 1\n",
      "stated , 1\n",
      "or , 1\n",
      "as some 1\n",
      "some suggested 1\n",
      "suggested , 1\n",
      "a swift 1\n",
      "swift italian 1\n",
      "italian poison 1\n",
      "poison administered 1\n",
      "administered in 1\n",
      "a cup 5\n",
      "cup of 3\n",
      "of spiced 1\n",
      "spiced wine 1\n",
      "wine , 2\n",
      ", slew 1\n",
      "slew , 1\n",
      ", within 1\n",
      "within an 1\n",
      "an hour 1\n",
      "hour of 4\n",
      "of her 17\n",
      "her wakening 1\n",
      "wakening , 1\n",
      ", the 70\n",
      "the white 16\n",
      "white girl 1\n",
      "girl who 1\n",
      "had given 6\n",
      "given him 2\n",
      "him birth 1\n",
      "birth , 1\n",
      "and as 27\n",
      "the trusty 1\n",
      "trusty messenger 1\n",
      "messenger who 1\n",
      "who bare 2\n",
      "bare the 1\n",
      "child across 1\n",
      "across his 1\n",
      "his saddle-bow 1\n",
      "saddle-bow stooped 1\n",
      "stooped from 1\n",
      "his weary 1\n",
      "weary horse 1\n",
      "horse and 2\n",
      "and knocked 6\n",
      "knocked at 2\n",
      "at the 55\n",
      "the rude 1\n",
      "rude door 1\n",
      "door of 3\n",
      "the goatherd 2\n",
      "goatherd 's 2\n",
      "'s hut 1\n",
      "hut , 1\n",
      "the body 14\n",
      "body of 8\n",
      "princess was 1\n",
      "was being 2\n",
      "being lowered 1\n",
      "lowered into 1\n",
      "into an 1\n",
      "an open 1\n",
      "open grave 1\n",
      "grave that 1\n",
      "that had 16\n",
      "been dug 1\n",
      "dug in 1\n",
      "a deserted 1\n",
      "deserted churchyard 1\n",
      "churchyard , 1\n",
      ", beyond 1\n",
      "beyond the 2\n",
      "city gates 1\n",
      "gates , 1\n",
      "a grave 1\n",
      "grave where 1\n",
      "where it 2\n",
      "was said 2\n",
      "said that 3\n",
      "that another 1\n",
      "another body 1\n",
      "body was 4\n",
      "was also 1\n",
      "also lying 1\n",
      "lying , 1\n",
      ", that 26\n",
      "that of 1\n",
      "a young 1\n",
      "young man 1\n",
      "man of 1\n",
      "of marvellous 1\n",
      "marvellous and 2\n",
      "and foreign 1\n",
      "foreign beauty 1\n",
      "beauty , 4\n",
      ", whose 3\n",
      "whose hands 1\n",
      "hands were 3\n",
      "were tied 1\n",
      "tied behind 1\n",
      "behind him 6\n",
      "him with 12\n",
      "a knotted 1\n",
      "knotted cord 1\n",
      "cord , 1\n",
      "whose breast 1\n",
      "breast was 1\n",
      "was stabbed 1\n",
      "stabbed with 1\n",
      "with many 2\n",
      "many red 1\n",
      "red wounds 1\n",
      "wounds . 1\n",
      ". such 1\n",
      "such , 1\n",
      ", at 5\n",
      "at least 2\n",
      "least , 1\n",
      ", was 6\n",
      "the story 1\n",
      "story that 1\n",
      "that men 2\n",
      "men whispered 1\n",
      "whispered to 3\n",
      "to each 12\n",
      "each other 15\n",
      "other . 4\n",
      ". certain 1\n",
      "certain it 1\n",
      "was that 1\n",
      "that the 36\n",
      "king , 17\n",
      "when on 1\n",
      "on his 19\n",
      "his deathbed 1\n",
      "deathbed , 1\n",
      ", whether 1\n",
      "whether moved 1\n",
      "moved by 1\n",
      "by remorse 1\n",
      "remorse for 1\n",
      "his great 2\n",
      "great sin 1\n",
      "sin , 1\n",
      "or merely 1\n",
      "merely desiring 1\n",
      "desiring that 1\n",
      "the kingdom 1\n",
      "kingdom should 1\n",
      "should not 2\n",
      "not pass 1\n",
      "pass away 1\n",
      "his line 1\n",
      "line , 1\n",
      "had had 2\n",
      "had the 4\n",
      "lad sent 1\n",
      "sent for 2\n",
      "for , 3\n",
      ", in 14\n",
      "the presence 2\n",
      "presence of 2\n",
      "the council 1\n",
      "council , 1\n",
      "had acknowledged 1\n",
      "acknowledged him 1\n",
      "him as 5\n",
      "as his 3\n",
      "his heir 1\n",
      "heir . 1\n",
      "and it 34\n",
      "it seems 1\n",
      "seems that 1\n",
      "that from 1\n",
      "the very 2\n",
      "very first 1\n",
      "first moment 1\n",
      "moment of 1\n",
      "his recognition 1\n",
      "recognition he 1\n",
      "shown signs 1\n",
      "signs of 1\n",
      "of that 3\n",
      "that strange 1\n",
      "strange passion 1\n",
      "passion for 2\n",
      "for beauty 1\n",
      "beauty that 1\n",
      "that was 23\n",
      "was destined 1\n",
      "destined to 1\n",
      "to have 9\n",
      "have so 1\n",
      "so great 7\n",
      "great an 1\n",
      "an influence 1\n",
      "influence over 1\n",
      "over his 5\n",
      "his life 3\n",
      "life . 1\n",
      ". those 1\n",
      "those who 13\n",
      "who accompanied 1\n",
      "accompanied him 1\n",
      "him to 8\n",
      "the suite 1\n",
      "suite of 1\n",
      "of rooms 1\n",
      "rooms set 1\n",
      "set apart 1\n",
      "apart for 1\n",
      "his service 2\n",
      "service , 2\n",
      ", often 1\n",
      "often spoke 1\n",
      "the cry 4\n",
      "cry of 9\n",
      "of pleasure 3\n",
      "pleasure that 3\n",
      "that broke 2\n",
      "broke from 4\n",
      "his lips 11\n",
      "lips when 1\n",
      "when he 43\n",
      "he saw 18\n",
      "saw the 11\n",
      "the delicate 1\n",
      "delicate raiment 1\n",
      "raiment and 1\n",
      "and rich 1\n",
      "rich jewels 1\n",
      "jewels that 2\n",
      "been prepared 1\n",
      "prepared for 2\n",
      "for him 8\n",
      "and of 8\n",
      "the almost 1\n",
      "almost fierce 1\n",
      "fierce joy 1\n",
      "joy with 1\n",
      "with which 3\n",
      "which he 9\n",
      "he flung 4\n",
      "flung aside 1\n",
      "aside his 1\n",
      "his rough 2\n",
      "rough leathern 1\n",
      "leathern tunic 2\n",
      "tunic and 2\n",
      "and coarse 1\n",
      "coarse sheepskin 1\n",
      "sheepskin cloak 2\n",
      "cloak . 1\n",
      ". he 64\n",
      "he missed 1\n",
      "missed , 1\n",
      "at times 1\n",
      "times the 1\n",
      "the fine 2\n",
      "fine freedom 1\n",
      "freedom of 1\n",
      "his forest 1\n",
      "forest life 1\n",
      "life , 5\n",
      "and was 12\n",
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      "a big 1\n",
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      "his waist 1\n",
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      ", beating 1\n",
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      "upon a 5\n",
      "a drum 1\n",
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      "the diver 2\n",
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      "out of 48\n",
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      "a pearl 2\n",
      "pearl in 1\n",
      "his right 2\n",
      "right hand 3\n",
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      "their oars 1\n",
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      "little bag 1\n",
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      "other , 8\n",
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      "over a 1\n",
      "a string 1\n",
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      "bright beads 1\n",
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      ". two 2\n",
      "two cranes 1\n",
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      "flew round 2\n",
      "round and 8\n",
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      "pearls of 1\n",
      "of ormuz 1\n",
      "ormuz , 1\n",
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      "whiter than 5\n",
      "than the 15\n",
      "morning star 2\n",
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      "strangely pale 2\n",
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      "little , 2\n",
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      "pearl , 1\n",
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      "great cry 4\n",
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      "the crag 1\n",
      "crag like 1\n",
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      "' 'nay 15\n",
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      "'thou art 5\n",
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      "shrieking through 1\n",
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      "faster than 2\n",
      "the wind 10\n",
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      "the slime 1\n",
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      "horrible things 1\n",
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      "with scales 1\n",
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      "sand , 3\n",
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      ": 'who 1\n",
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      "grew pale 4\n",
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      "'for what 1\n",
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      ": 'look 1\n",
      "'look in 1\n",
      "in this 11\n",
      "this mirror 3\n",
      "mirror , 3\n",
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      "emperor charles 1\n",
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      "of sea-green 1\n",
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      "onyx , 1\n",
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      "the shadow 5\n",
      "shadow of 6\n",
      "the doorway 2\n",
      "doorway , 1\n",
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      "watching him 3\n",
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      "arms to 5\n",
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      "? could 3\n",
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      "shadows of 2\n",
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      "petal the 1\n",
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      "? the 2\n",
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      "up to 4\n",
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      "white ivory 1\n",
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      "fingers i 1\n",
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      "soft serpent-skin 1\n",
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      "' plumage 1\n",
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      "'he combed 1\n",
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      "stature of 2\n",
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      "seven beryls 1\n",
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      "this the 5\n",
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      "? '' 4\n",
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      ". '' 14\n",
      "'' ' 3\n",
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      "`` let 2\n",
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      "lord heal 2\n",
      "heal his 2\n",
      "his servant 2\n",
      "servant , 3\n",
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      "'so i 2\n",
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      ": `` 2\n",
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      "of wisdom 3\n",
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      "into thee 2\n",
      "thee again 1\n",
      "be wiser 1\n",
      "the wise 1\n",
      "wise men 1\n",
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      "be thine 3\n",
      "thine . 3\n",
      ". suffer 2\n",
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      "thou . 1\n",
      ". 'love 4\n",
      "'love is 5\n",
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      "mermaid loves 2\n",
      "loves me 2\n",
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      "nothing better 2\n",
      "better , 2\n",
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      "the south 2\n",
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      "dusty red-dyed 1\n",
      "red-dyed highways 1\n",
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      "the pilgrims 1\n",
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      "i journey 2\n",
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      "my eyes 1\n",
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      "my feet 2\n",
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      "a bronze 1\n",
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      "a bazaar 1\n",
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      ". across 2\n",
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      "gay lanterns 1\n",
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      "of paper 1\n",
      "paper flutter 1\n",
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      "large butterflies 1\n",
      "butterflies . 1\n",
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      "strings of 3\n",
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      "their cool 1\n",
      "cool fingers 1\n",
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      "the indian 1\n",
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      "oil of 1\n",
      "red roses 1\n",
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      "throw pinches 1\n",
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      "frankincense upon 1\n",
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      "charcoal brazier 1\n",
      "brazier and 1\n",
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      "air sweet 1\n",
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      "syrian who 1\n",
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      "hands a 1\n",
      "thin rod 1\n",
      "rod like 1\n",
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      ". grey 1\n",
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      "its odour 1\n",
      "odour as 1\n",
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      "burned was 1\n",
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      "the pink 1\n",
      "pink almond 1\n",
      "almond in 1\n",
      ". others 1\n",
      "others sell 1\n",
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      "with creamy 1\n",
      "creamy blue 1\n",
      "blue turquoise 1\n",
      "turquoise stones 1\n",
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      "anklets of 1\n",
      "brass wire 1\n",
      "wire fringed 1\n",
      "little pearls 1\n",
      "and tigers 1\n",
      "tigers ' 1\n",
      "' claws 1\n",
      "claws set 1\n",
      "set in 3\n",
      "in gold 2\n",
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      "claws of 2\n",
      "that gilt 1\n",
      "gilt cat 1\n",
      "the leopard 1\n",
      "leopard , 1\n",
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      "gold also 1\n",
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      "of pierced 2\n",
      "pierced emerald 1\n",
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      "finger-rings of 1\n",
      "of hollowed 1\n",
      "hollowed jade 1\n",
      "jade . 2\n",
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      "the guitar 1\n",
      "guitar , 1\n",
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      "their white 1\n",
      "white smiling 1\n",
      "smiling faces 1\n",
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      "out at 1\n",
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      ". 'of 1\n",
      "'of a 2\n",
      "a truth 3\n",
      "truth thou 2\n",
      "the wine-sellers 1\n",
      "wine-sellers elbow 1\n",
      "elbow their 1\n",
      "their way 3\n",
      "way through 3\n",
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      ". most 1\n",
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      "wine of 2\n",
      "of schiraz 1\n",
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      "as honey 1\n",
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      "little metal 1\n",
      "metal cups 1\n",
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      "strew rose 1\n",
      "rose leaves 1\n",
      "leaves upon 1\n",
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      "the fruitsellers 1\n",
      "fruitsellers , 1\n",
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      "of fruit 1\n",
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      ": ripe 1\n",
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      "purple flesh 1\n",
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      "of musk 1\n",
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      ", citrons 1\n",
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      "and rose-apples 1\n",
      "rose-apples and 1\n",
      "white grapes 1\n",
      ", round 1\n",
      "round red-gold 1\n",
      "red-gold oranges 1\n",
      "oranges , 2\n",
      "and oval 1\n",
      "oval lemons 1\n",
      "lemons of 1\n",
      "green gold 1\n",
      "once i 1\n",
      "an elephant 1\n",
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      "painted with 2\n",
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      "vermilion and 1\n",
      "and turmeric 1\n",
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      "a net 1\n",
      "net of 1\n",
      "silk cord 1\n",
      "cord . 1\n",
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      "opposite one 1\n",
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      "booths and 1\n",
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      "throat lest 1\n",
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      "red foam 1\n",
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      "bright sweat 1\n",
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      "knees shook 1\n",
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      "red porphyry 1\n",
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      "with lamps 1\n",
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      "the brim 1\n",
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      "silver pieces 1\n",
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      "cedar , 1\n",
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      "my house 1\n",
      "of treasure 1\n",
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      "? trouble 1\n",
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      "soul tempt 2\n",
      "tempt him 3\n",
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      ", 'love 1\n",
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      "morning the 1\n",
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      "been troubled 1\n",
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      "went the 2\n",
      "monks and 2\n",
      "the musicians 2\n",
      "musicians , 2\n",
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      "the swingers 2\n",
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      "of censers 2\n",
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      "great company 2\n",
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      "here with 1\n",
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      "corner of 4\n",
      "field of 2\n",
      "the fullers 3\n",
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      "no sweet 1\n",
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      "deep pit 1\n",
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      "a holy 1\n",
      "holy day 1\n",
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      "people the 1\n",
      "the wounds 2\n",
      "wounds of 1\n",
      "and speak 1\n",
      "them about 1\n",
      "the wrath 3\n",
      "wrath of 3\n",
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      "robed himself 1\n",
      "his robes 1\n",
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      "himself before 1\n",
      "altar was 1\n",
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      "strange flowers 1\n",
      "flowers that 2\n",
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      "never had 1\n",
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      ". strange 1\n",
      "strange were 1\n",
      "they to 1\n",
      "at , 4\n",
      "of curious 1\n",
      "curious beauty 1\n",
      "their beauty 1\n",
      "beauty troubled 1\n",
      "troubled him 2\n",
      "their odour 2\n",
      "odour was 2\n",
      "was sweet 2\n",
      "sweet in 2\n",
      "felt glad 1\n",
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      "understood not 1\n",
      "not why 1\n",
      "was glad 2\n",
      "glad . 2\n",
      "had opened 1\n",
      "the tabernacle 1\n",
      "tabernacle , 1\n",
      "and incensed 1\n",
      "incensed the 1\n",
      "the monstrance 1\n",
      "monstrance that 1\n",
      "fair wafer 1\n",
      "again behind 1\n",
      "the veil 1\n",
      "of veils 1\n",
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      ", desiring 1\n",
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      "them of 1\n",
      "the beauty 1\n",
      "beauty of 1\n",
      "white flowers 1\n",
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      "spake not 1\n",
      "but of 1\n",
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      "whose name 1\n",
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      "he so 1\n",
      "so spake 1\n",
      "spake , 1\n",
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      "his word 1\n",
      "word the 1\n",
      "people wept 1\n",
      "the sacristy 1\n",
      "sacristy , 1\n",
      "eyes were 3\n",
      "of tears 2\n",
      "the deacons 1\n",
      "deacons came 1\n",
      "to unrobe 1\n",
      "unrobe him 1\n",
      "the alb 1\n",
      "alb and 1\n",
      "the girdle 1\n",
      "girdle , 1\n",
      "the maniple 1\n",
      "maniple and 1\n",
      "the stole 1\n",
      "stole . 1\n",
      "stood as 1\n",
      "had unrobed 1\n",
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      "'thou canst 2\n",
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      "thou see 1\n",
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      ", seeking 1\n",
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      "my suffering 1\n",
      "suffering is 1\n",
      "can bear 1\n",
      "bear . 1\n",
      "thy forgiveness 1\n",
      "beggar-woman put 1\n",
      "hand on 2\n",
      "'rise , 2\n",
      "leper put 1\n",
      "' also 1\n",
      "! they 1\n",
      "a queen 1\n",
      "queen said 1\n",
      "thy father 1\n",
      "father whom 1\n",
      "succoured . 1\n",
      "king said 1\n",
      "mother whose 1\n",
      "whose feet 1\n",
      "feet thou 1\n",
      "hast washed 1\n",
      "washed with 1\n",
      "thy tears 1\n",
      "and clothed 1\n",
      "clothed him 1\n",
      "in fair 1\n",
      "sceptre in 1\n",
      "river he 1\n",
      "ruled , 1\n",
      "was its 1\n",
      ". much 1\n",
      "much justice 1\n",
      "mercy did 1\n",
      "he show 1\n",
      "evil magician 1\n",
      "magician he 1\n",
      "he banished 1\n",
      "banished , 1\n",
      "wife he 1\n",
      "he sent 1\n",
      "sent many 1\n",
      "many rich 1\n",
      "rich gifts 1\n",
      "gifts , 1\n",
      "their children 1\n",
      "children he 1\n",
      "gave high 1\n",
      "high honour 1\n",
      "honour . 1\n",
      "he suffer 1\n",
      "suffer any 1\n",
      "any to 1\n",
      "to bird 1\n",
      "bird or 1\n",
      "or beast 1\n",
      "beast , 1\n",
      "but taught 1\n",
      "taught love 1\n",
      "love and 1\n",
      "and loving-kindness 1\n",
      "loving-kindness and 1\n",
      "and charity 1\n",
      "charity , 1\n",
      "poor he 1\n",
      "gave bread 1\n",
      "the naked 1\n",
      "naked he 1\n",
      "gave raiment 1\n",
      "was peace 1\n",
      "peace and 1\n",
      "and plenty 1\n",
      "plenty in 1\n",
      "yet ruled 1\n",
      "ruled he 1\n",
      "not long 1\n",
      "his suffering 1\n",
      "suffering , 1\n",
      "so bitter 1\n",
      "bitter the 1\n",
      "fire of 1\n",
      "his testing 1\n",
      "testing , 1\n",
      "for after 1\n",
      "he died 1\n",
      "who came 1\n",
      "came after 1\n",
      "after him 1\n",
      "him ruled 1\n",
      "ruled evilly 1\n",
      "evilly . 1\n",
      "...\n"
     ]
    }
   ],
   "source": [
    "for ngram in list(myBigramFD.items()):\n",
    "    print(\" \".join(ngram[0]), ngram[1])\n",
    "print(\"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can remove the [:20] restrictor above and print out the entire frequency profile. If you select and copy the profile to your clipboard, you can paste it into your favorite spreadsheet software and sort, analyze, and study the distribution in many interesting ways."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of running the frequency profile through a loop we can also use a list comprehension construction in Python to generate a list of tuples with the n-gram and its frequency:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(', and', 1271), ('of the', 354), ('and the', 287), ('. and', 205), (\". '\", 193), ('in the', 158), (\", '\", 155), ('to the', 149), ('him ,', 146), ('. the', 112), (\"' and\", 108), ('the young', 107), (', but', 96), ('and he', 89), (', for', 85), (\"? '\", 84), ('on the', 80), ('said to', 79), ('to him', 77), ('and said', 77), (', he', 73), ('and when', 71), ('young fisherman', 71), (', the', 70), ('from the', 69), ('. he', 64), ('he had', 63), ('through the', 58), ('into the', 55), ('at the', 55), ('i will', 55), ('the little', 54), ('and his', 53), ('it was', 52), ('his soul', 51), ('said ,', 49), ('of a', 48), ('out of', 48), ('that he', 47), ('and a', 47), ('when the', 47), ('for the', 47), ('in a', 46), ('with the', 45), ('it ,', 45), ('me ,', 45), ('. but', 44), ('him .', 44), ('he was', 43), ('with a', 43), ('when he', 43), (\"' i\", 42), ('it .', 42), ('the star-child', 41), ('of his', 41), ('i have', 41), ('her ,', 39), ('had been', 38), ('the sea', 38), (\"' he\", 37), ('by the', 36), ('that the', 36), ('but the', 36), ('to his', 36), ('them ,', 35), ('thee ,', 35), ('the city', 34), ('and it', 34), ('and in', 34), ('and they', 34), ('the infanta', 33), ('a little', 32), ('i am', 32), (\"' she\", 32), (', nor', 32), ('in his', 31), (\"' but\", 31), ('and i', 31), ('the forest', 30), (', who', 30), ('as he', 30), ('and she', 30), ('me .', 30), (', as', 29), ('a great', 29), ('. they', 29), ('of them', 28), ('from his', 28), ('over the', 28), (\"' said\", 28), ('to her', 28), ('soul ,', 28), ('. i', 28), (', or', 27), ('and as', 27), ('that i', 27), ('piece of', 27), (', that', 26), ('. it', 26), ('cried ,', 26)]\n"
     ]
    }
   ],
   "source": [
    "ngrams = [ (\" \".join(ngram), myBigramFD[ngram]) for ngram in myBigramFD ]\n",
    "print(ngrams[:100])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can generate an increasing frequency profile using the sort function on the second element of the tuple list, that is on the frequency:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('pomegranates contents', 1), ('contents :', 1), ('king the', 1), ('infanta the', 1), ('soul the', 1), ('star-child the', 1), ('king [', 1), ('to margaret', 1), ('margaret lady', 1), ('lady brooke', 1), ('brooke --', 1), ('the ranee', 1), ('ranee of', 1), ('of sarawak', 1), ('sarawak ]', 1), ('night before', 1), ('day fixed', 1), ('fixed for', 1), ('king was', 1), ('was sitting', 1)]\n",
      "...\n"
     ]
    }
   ],
   "source": [
    "sortedngrams = sorted(ngrams, key=lambda x: x[1])\n",
    "print(sortedngrams[:20])\n",
    "print(\"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can increase the speed of this *sorted* call by using the *itemgetter()* function in the *operator* module. Let us import this function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from operator import itemgetter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now define the sort-key for *sorted* using the *itemgetter* function and selecting with 1 the second element in the tuple. Remember that the enumeration of elements in lists or tuples in Python starts at 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('pomegranates contents', 1), ('contents :', 1), ('king the', 1), ('infanta the', 1), ('soul the', 1), ('star-child the', 1), ('king [', 1), ('to margaret', 1), ('margaret lady', 1), ('lady brooke', 1), ('brooke --', 1), ('the ranee', 1), ('ranee of', 1), ('of sarawak', 1), ('sarawak ]', 1), ('night before', 1), ('day fixed', 1), ('fixed for', 1), ('king was', 1), ('was sitting', 1)]\n",
      "...\n"
     ]
    }
   ],
   "source": [
    "sortedngrams = sorted(ngrams, key=itemgetter(1))\n",
    "print(sortedngrams[:20])\n",
    "print(\"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A decreasing frequency profile can be generated using another parameter to *sorted*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(', and', 1271), ('of the', 354), ('and the', 287), ('. and', 205), (\". '\", 193), ('in the', 158), (\", '\", 155), ('to the', 149), ('him ,', 146), ('. the', 112), (\"' and\", 108), ('the young', 107), (', but', 96), ('and he', 89), (', for', 85), (\"? '\", 84), ('on the', 80), ('said to', 79), ('to him', 77), ('and said', 77)]\n",
      "...\n"
     ]
    }
   ],
   "source": [
    "sortedngrams = sorted(ngrams, key=itemgetter(1), reverse=True)\n",
    "print(sortedngrams[:20])\n",
    "print(\"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can pretty-print the decreasing frequency profile:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ", and 1271\n",
      "of the 354\n",
      "and the 287\n",
      ". and 205\n",
      ". ' 193\n",
      "in the 158\n",
      ", ' 155\n",
      "to the 149\n",
      "him , 146\n",
      ". the 112\n",
      "' and 108\n",
      "the young 107\n",
      ", but 96\n",
      "and he 89\n",
      ", for 85\n",
      "? ' 84\n",
      "on the 80\n",
      "said to 79\n",
      "to him 77\n",
      "and said 77\n",
      "...\n"
     ]
    }
   ],
   "source": [
    "sortedngrams = sorted(ngrams, key=itemgetter(1), reverse=True)\n",
    "for t in sortedngrams[:20]:\n",
    "    print(t[0], t[1])\n",
    "print(\"...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "young fisherman 0.0018622462361642972\n",
      "young king 0.0006294916854639878\n",
      "thou hast 0.0005508052247809893\n",
      "thou art 0.0005245764045533231\n",
      "little dwarf 0.000498347584325657\n",
      "thou shalt 0.00034097466295966006\n",
      "little mermaid 0.0003147458427319939\n",
      "soul answered 0.0003147458427319939\n",
      "give thee 0.0002622882022766616\n",
      "let us 0.0002622882022766616\n",
      "get thee 0.00023605938204899544\n",
      "soul said 0.00023605938204899544\n",
      "yellow gold 0.00023605938204899544\n",
      "whole world 0.00020983056182132927\n",
      "art thou 0.00020983056182132927\n",
      "day long 0.00020983056182132927\n",
      "'thou hast 0.00020983056182132927\n",
      "thee gone 0.00020983056182132927\n",
      "white rose 0.00020983056182132927\n",
      "white gold 0.00020983056182132927\n"
     ]
    }
   ],
   "source": [
    "total = float(sum(myBigramFD.values()))\n",
    "exceptions = [\"]\", \"[\", \"--\", \",\", \".\", \"'s\", \"?\", \"!\", \"'\", \"'ye\"]\n",
    "results = []\n",
    "for x in myBigramFD:\n",
    "    if x[0] in exceptions or x[1] in exceptions:\n",
    "        continue\n",
    "    if x[0] in stopwords or x[1] in stopwords:\n",
    "        continue\n",
    "    results.append( (x[0], x[1], myBigramFD[x]/total) )\n",
    "sortedresults = sorted(results, key=itemgetter(2), reverse=True)\n",
    "for x in sortedresults[:20]:\n",
    "    print(x[0], x[1], x[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be continued..."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(C) 2017-2024 by [Damir Cavar](http:https://damir.cavar.me/) <<dcavar@iu.edu>>"
   ]
  }
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