A Guided Tour of Proto-Elamite.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Introduction\n",
    "\n",
    "Welcome to _A Guided Tour of Proto-Elamite_! This is a suite of data exploration tools intended to provide an easy introduction to working with the proto-Elamite corpus. \n",
    "This notebook reproduces and elaborates on the results of Born et al. (2019), and we recommend you consult the paper for background information prior to using this notebook.\n",
    "\n",
    "## 1.1. Using this Notebook\n",
    "\n",
    "If you are running this notebook locally or on Binder, click on a block of code and press Shift-Enter to run it. The output will print out beneath the code. If you are on github or nbviewer, interactivity is disabled, but you can still see the default output from each cell.\n",
    "\n",
    "Readers are encouraged to edit parameter settings to see how they affect the output. Parameters of interest are usually explained with comments at the beginning of each code block. We summarize the function of each code block in the surrounding text, but readers with a programming background will find additional implementation details left as comments in the code.\n",
    "\n",
    "Some code blocks rely on data computed in previous cells, so it is recommended to run them in the order they appear."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Setup\n",
    "\n",
    "The transcribed proto-Elamite corpus used by Born et al. (2019) is available in the file `data/cdli_atf_20180602.txt.bz2`. Importing the `pype` package will load this data automatically:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Packages imported.\n"
     ]
    }
   ],
   "source": [
    "import pype # Main set of tools for working with proto-Elamite.\n",
    "import pe_toolkit_utils as util # Helper functions; moved to their own package to reduce clutter in the notebook.\n",
    "print(\"Packages imported.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`pype` provides a basic interface for accessing `Tablet` objects, which themselves expose methods for retrieving information about a text: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing data about an example tablet:\n",
      "\tCDLI #P009402:\n",
      "\tTablet is sealed?\tFalse\n",
      "\tTablet header:\t\tNone\n",
      "\tTransliteration:\n",
      "\t   [...] , \n",
      "\t   [...] , 2(N01)\n",
      "\t   X , 2(N01)#\n",
      "\t   [...] , 1(N01)#\n",
      "\t   M131~E? , 2(N14)\n",
      "\t   M136~W , 1(N01)#\n",
      "\t   X , 3(N01)#\n",
      "\t   X , [...]\n"
     ]
    }
   ],
   "source": [
    "print(\"Printing data about an example tablet:\")\n",
    "# pype.tablets is a list of Tablet objects:\n",
    "for tablet in pype.tablets:\n",
    "    # Public members include the tablet's CDLI catalogue number, \n",
    "    # a boolean recording whether or not the tablet is sealed, \n",
    "    # and the value of the tablet's header if it has one.\n",
    "    print(\"\\tCDLI #%s:\\n\\tTablet is sealed?\\t%s\\n\\tTablet header:\\t\\t%s\" % (\n",
    "        tablet.cdliNo,\n",
    "        tablet.sealed,\n",
    "        tablet.header\n",
    "    ))\n",
    "    \n",
    "    # Tablets will prettyprint by default:\n",
    "    # print( tablet )\n",
    "    \n",
    "    # It is possible to retrieve the contents of the tablet as a \n",
    "    # space-delimited string of sign names:\n",
    "    # linearized_tablet = tablet.linearize( excludeNumeric = False )\n",
    "    # To ignore the numeric signs, use excludeNumeric = True.\n",
    "    \n",
    "    # Retrieve the contents of the tablet line-by-line:\n",
    "    print(\"\\tTransliteration:\")\n",
    "    for entry, number in tablet.getLines( excludeNumeric = False ):\n",
    "        print('\\t  ',entry,',',number)\n",
    "    # To ignore the numeric signs, use excludeNumeric = True:\n",
    "    # for entry in tablet.getLines( excludeNumeric = True ):\n",
    "    #     print('\\t  ',entry)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Corpus Overview\n",
    "\n",
    "We begin by printing out basic information about the corpus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of texts: 1467\n",
      "Number of texts with readable, non-numeric content: 1399\n",
      "\n",
      "Number of signs: 1672\n",
      "Number of numeric signs: 49\n",
      "Number of non-numeric signs: 1623\n",
      "Number of basic signs: 287\n",
      "Number of sign variants: 1087\n",
      "Number of compound signs: 249\n"
     ]
    }
   ],
   "source": [
    "# Some tablets only contain numeric or unreadable signs.\n",
    "# Let's remove them from the corpus:\n",
    "import re\n",
    "unreadable_signs_regex = r\"\\[...\\]|X\\??\"\n",
    "tablets_with_real_content = [t for t in pype.tablets \n",
    "                             if re.sub( unreadable_signs_regex, \"\", \n",
    "                                       t.linearize( excludeNumeric=True ).upper() \n",
    "                                      ).strip() != '']\n",
    "\n",
    "# Signs are often annotated to mark whether they are damaged, \n",
    "# hard to read, inferred from context, etc. This code removes\n",
    "# those annotations to find a list of distinct signs used in the corpus:\n",
    "transliteration_annotations_regex = r\"\\[...\\]|\\?|#|!|\\[|\\]|<|>\"\n",
    "signary = set(\n",
    "              re.sub( transliteration_annotations_regex, \"\",\n",
    "                     sign.upper()\n",
    "                    ) for t in pype.tablets \n",
    "                      for sign in t.linearize( excludeNumeric = False ).split()\n",
    "    ).difference( {'', 'X', '...'} ) # Don't count 'X' and '...' as signs\n",
    "\n",
    "# Extract the numeric signs from the signary:\n",
    "numeric_signary = set(s for s in signary if s.startswith(\"N\"))\n",
    "\n",
    "# This splits the signary into basic signs and sign variants.\n",
    "# Sign variants are marked with a \"~\".\n",
    "basic_signs = set(s for s in signary if '~' not in s and '+' not in s).difference(numeric_signary)\n",
    "sign_variants = set(s for s in signary if '~' in s and '+' not in s).difference(numeric_signary)\n",
    "\n",
    "# Sometimes signs are combined to form a compound glyph.\n",
    "# The names of the constituent signs are combined with +\n",
    "# and the whole compound is enclosed by |, as in |M195+M057|\n",
    "# A few transliterations mistakenly omit the '|' character, so\n",
    "# the safest way to find compound glyphs is by searching for \n",
    "# signs containing '+'. \n",
    "compound_signs = set(s for s in signary if '+' in s).difference(numeric_signary)\n",
    "\n",
    "print( \"Number of texts:\", \n",
    "      len(pype.tablets) )\n",
    "print( \"Number of texts with readable, non-numeric content:\", \n",
    "      len( tablets_with_real_content ) )\n",
    "print()\n",
    "print( \"Number of signs:\", \n",
    "      len(signary) )\n",
    "print( \"Number of numeric signs:\",\n",
    "      len(numeric_signary) )\n",
    "print( \"Number of non-numeric signs:\",\n",
    "      len(signary.difference(numeric_signary)) )\n",
    "print( \"Number of basic signs:\",\n",
    "      len(basic_signs))\n",
    "print( \"Number of sign variants:\",\n",
    "      len(sign_variants))\n",
    "print( \"Number of compound signs:\",\n",
    "     len(compound_signs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although there are a large number of distinct signs, the proto-Elamite corpus is relatively small, with only 1399 texts containing readable non-numeric signs. Many of these texts are also very short, as we can see by plotting the distribution of tablet lengths:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import necessary packages:\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "\n",
    "# Compute the length of each tablet, ignoring lacunae and unreadable signs:\n",
    "length_in_signs = [ \n",
    "    len( \n",
    "        [ sign for sign in t.linearize( excludeNumeric = False ).upper().split() if sign != '[...]' and sign != 'X'] \n",
    "    ) for t in pype.tablets ]\n",
    "\n",
    "length_in_entries = [\n",
    "    len(\n",
    "        list(t.getLines( excludeNumeric = False ))\n",
    "    ) for t in pype.tablets\n",
    "]\n",
    "\n",
    "def draw_histogram( data, ax, num_bins = 10 ):\n",
    "    # Bin data and get label for each bin:\n",
    "    bins, labels = np.histogram(data, bins=num_bins )\n",
    "    labels = [ \"%d--%d\"%(labels[i]+1,labels[i+1]) for i in range(len(labels)-1) ]\n",
    "\n",
    "    # Draw the plot:\n",
    "    x = np.arange( num_bins )\n",
    "    ax.bar( x, bins )\n",
    "    ax.set_xticks( x )\n",
    "    ax.set_xticklabels( labels )\n",
    "\n",
    "    # Annotate with labels showing exact height of each bar:\n",
    "    for i, n in enumerate( bins ):\n",
    "        adjust_x = ((np.log(n) if n != 0 else 0)/50)\n",
    "        adjust_y = 0.01 * max( bins )\n",
    "        ax.text(i - adjust_x, n + adjust_y, str(n), color='k')\n",
    "\n",
    "# Draw the two histograms:\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(10,10))    \n",
    "\n",
    "draw_histogram( length_in_entries, ax1 )\n",
    "ax1.set_title(\"Tablet Length Distribution\")\n",
    "ax1.set_xlabel(\"Tablet length (number of entries)\")\n",
    "ax1.set_ylabel(\"Number of tablets\")\n",
    "\n",
    "draw_histogram( length_in_signs, ax2 )\n",
    "ax2.set_xlabel(\"Tablet length (number of signs)\")\n",
    "ax2.set_ylabel(\"Number of tablets\")\n",
    "\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histograms above show that, regardless of whether we measure a tablet's length as the number of signs it contains or the number of entries it contains, the corpus is overwhelmingly comprised of short texts. We can get a better sense for the length distribution by computing the average and maximum text length and number of numeric and non-numeric signs in each text:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean text length: 27.48 signs (10.15 are non-numeric on average)\n",
      "Max text length: 724 signs (198 are non-numeric)\n"
     ]
    }
   ],
   "source": [
    "length_in_nonnumeric_signs = [ \n",
    "    len( \n",
    "        [ sign for sign in t.linearize( excludeNumeric = True ).split() if sign != '[...]' and sign != 'X' ] \n",
    "    ) for t in pype.tablets ]\n",
    "\n",
    "mean_len = sum(length_in_signs) / len(length_in_signs)\n",
    "mean_len_nonnumeric = sum(length_in_nonnumeric_signs) / len(length_in_nonnumeric_signs)\n",
    "\n",
    "max_len_i = np.argmax(length_in_signs)\n",
    "max_len = length_in_signs[max_len_i]\n",
    "max_len_nonnumeric = length_in_nonnumeric_signs[max_len_i]\n",
    "\n",
    "print(\"Mean text length: %.2f signs (%.2f are non-numeric on average)\" % (mean_len, mean_len_nonnumeric))\n",
    "print(\"Max text length: %d signs (%d are non-numeric)\" % (max_len,max_len_nonnumeric))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that most texts are less than 30 signs long, and on average well over half of the signs in a tablet are actually numeric. The non-numeric signs, which are of most interest for the purposes of decipherment, make up a modest proportion of an already small corpus.\n",
    "\n",
    "# 4. n-Gram Counts\n",
    "\n",
    "`pype` provides an `ngrams` function to count the number of times a sign or string of signs occurs in the corpus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "M066 occurs 243 times in the corpus.\n",
      "M066 and its variants occur 293 times in the corpus.\n",
      "M388 is followed by M066 17 times in the corpus.\n"
     ]
    }
   ],
   "source": [
    "unigram_counts        = pype.ngrams( 1, normalizeSigns = False, excludeNumeric = False, excludeCorrections = True )\n",
    "# if normalizeSigns = True, pype.ngrams will count sign variants as \n",
    "# instances of the corresponding base sign instead.\n",
    "normed_unigram_counts = pype.ngrams( 1, normalizeSigns = True,  excludeNumeric = False, excludeCorrections = True )\n",
    "bigram_counts         = pype.ngrams( 2, normalizeSigns = False, excludeNumeric = False, excludeCorrections = True )\n",
    "\n",
    "# if excludeCorrections = True, pype.ngrams will not count signs \n",
    "# which have been identified as scribal errors and corrected by\n",
    "# the transliterator.\n",
    "\n",
    "print( 'M066 occurs %d times in the corpus.' \n",
    "      % ( unigram_counts[ ('M066',) ] ) \n",
    "     )\n",
    "print( 'M066 and its variants occur %d times in the corpus.'\n",
    "      % ( normed_unigram_counts[ ('M066',) ] ) \n",
    "     )\n",
    "print( 'M388 is followed by M066 %d times in the corpus.'\n",
    "      % ( bigram_counts[ ('M388', 'M066') ] ) \n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use this to plot the most frequent $n$-grams for any value of $n$. Edit `N` and `PLOT_SIZE` in the following code block to get longer $n$-grams and adjust the size of the graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#################################################################\n",
    "# Edit this to change the length of n-grams in the graph:\n",
    "N = 1\n",
    "#################################################################\n",
    "# Edit this to adjust the size of the plot. May be slow for large plots.\n",
    "PLOT_SIZE = 10\n",
    "\n",
    "# Get n-gram counts\n",
    "ngrams_dict = pype.ngrams( N, normalizeSigns = False, excludeNumeric=True, excludeHeader=False, excludeCorrections=True )\n",
    "# Convert ngram dictionary to list and sort by frequency:\n",
    "ngrams_list = sorted([ (sign, ngrams_dict[sign]) for sign in ngrams_dict ],\n",
    "                     key=lambda sign_count:sign_count[1] )[-PLOT_SIZE:]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,PLOT_SIZE/2.5))\n",
    "\n",
    "# Bar graph of ngram counts:\n",
    "x      = [ count for _,count in ngrams_list ]\n",
    "labels = [ ', '.join(sign) for sign,_ in ngrams_list ]\n",
    "y      = np.arange(len(x))\n",
    "ax.barh(y, x, height=0.5)\n",
    "\n",
    "# Label each bar with the actual ngram count:\n",
    "for i, n in enumerate(x):\n",
    "    adjust_x = 0.005 * max( x )\n",
    "    adjust_y = -0.125\n",
    "    ax.text(n + adjust_x, i + adjust_y, str(n), color='k')\n",
    "\n",
    "# Axis labels\n",
    "ax.set_xlabel(\"n-gram Frequency\", fontsize=16)\n",
    "\n",
    "ax.set_yticks(y)\n",
    "ax.set_yticklabels(labels)\n",
    "ax.set_title(\"%d-grams sorted by frequency\"%(N), fontsize=18)\n",
    "# Cut off extra whitespace at the edges\n",
    "plt.ylim([-1,max(y) + 1])\n",
    "\n",
    "plt.tick_params(axis='y', which='major', labelsize=16)\n",
    "plt.tick_params(axis='x', which='major', labelsize=14)\n",
    "plt.tight_layout()\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Hapaxes\n",
    "\n",
    "A large proportion of the signary consists of hapaxes, signs which occur only once in the entire corpus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 745 hapaxes in the signary, including M393~G, |M009+M140~A|, M049~B, M251~E, M316~Q...\n"
     ]
    }
   ],
   "source": [
    "hapaxes = set(s for s in unigram_counts if unigram_counts[s] == 1)\n",
    "print(\"There are %d hapaxes in the signary, including %s...\"\n",
    "      %(len(hapaxes), \", \".join(s[0] for s in list(hapaxes)[:5]))\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many of these hapaxes are variants of other signs, and future work may be able to reduce the size of the signary by identifying whether these variations are meaningful or merely cosmetic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Pointwise Mutual Information (PMI)\n",
    "\n",
    "Pointwise mutual information (PMI) is generally useful for identifying meaningful collocations in text. Rare $n$-grams tend to have disproportionately high PMI, however, so on a corpus such as this one ranking $n$-grams by PMI simply produces a list of hapax $n$-grams. The following code block shows the trigrams with the highest PMI, restricted to trigrams which occur at least 5 times in the corpus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#################################################################\n",
    "# Edit this to change the length of n-grams in the graph:\n",
    "N = 3\n",
    "# Exclude ngrams which do not occur at least THRESHOLD times:\n",
    "THRESHOLD = 5\n",
    "#################################################################\n",
    "# Edit this to adjust the size of the plot. May be slow for large plots.\n",
    "PLOT_SIZE = 10\n",
    "\n",
    "ngram_dicts = dict()\n",
    "ngram_Zs    = dict()\n",
    "for i in range(N+1):\n",
    "    ngram_dicts[i] = pype.ngrams( i, normalizeSigns = False, excludeNumeric=True, excludeHeader=False, excludeCorrections=True )\n",
    "    ngram_Zs[i] = sum(ngram_dicts[i][s] for s in ngram_dicts[i])\n",
    "\n",
    "def ngram_prob( ngram ):\n",
    "    n = len(ngram)\n",
    "    return ngram_dicts[n][ngram] / ngram_Zs[n]\n",
    "                \n",
    "def pmi( ngram ):\n",
    "    ctx,s = ngram[:-1],ngram[-1]\n",
    "    return (np.log( ngram_prob(ngram) ) - np.log( (ngram_prob(ctx)*ngram_prob((s,))) )) / np.log(2)\n",
    "\n",
    "ngrams_by_pmi = sorted([(g, pmi(g)) for g in ngram_dicts[N] if ngram_dicts[N][g] >= THRESHOLD],key=lambda x:x[1])\n",
    "ngrams_list = ngrams_by_pmi\n",
    "\n",
    "ngrams_list = [ (s,c) for s,c in ngrams_list ][-PLOT_SIZE:]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,PLOT_SIZE/2.5))\n",
    "\n",
    "# Bar graph of ngram counts:\n",
    "x      = [ count for _,count in ngrams_list ]\n",
    "labels = [ ', '.join(sign) for sign,_ in ngrams_list ]\n",
    "y      = np.arange(len(x))\n",
    "ax.barh(y, x, height=0.5)\n",
    "\n",
    "# Label each bar with the actual ngram count:\n",
    "for i, n in enumerate(x):\n",
    "    adjust_x = 0.005 * max( x )\n",
    "    adjust_y = -0.125\n",
    "    ax.text(n + adjust_x, i + adjust_y, \"%.02f (%d)\"%(n, ngram_dicts[N][ngrams_list[i][0]]), color='k')\n",
    "\n",
    "# Axis labels\n",
    "ax.set_xlabel(\"PMI (ngram frequency in parentheses)\", fontsize=16)\n",
    "\n",
    "ax.set_yticks(y)\n",
    "ax.set_yticklabels(labels)\n",
    "ax.set_title(\"%d-grams sorted by PMI\"%(N), fontsize=18)\n",
    "# Cut off extra whitespace at the edges\n",
    "plt.ylim([-1,max(y) + 1])\n",
    "\n",
    "plt.tick_params(axis='y', which='major', labelsize=16)\n",
    "plt.tick_params(axis='x', which='major', labelsize=14)\n",
    "plt.tight_layout()\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In English, proper nouns tend to have high PMI; at a glance, a similar trend may hold true in proto-Elamite. Many of the signs appearing in the above plot have been speculated to have syllabic or logographic readings which are used for writing anthroponyms (Meriggi 1971:173-174 _La scrittura proto-elamica. Parte Ia:  La scrittura e il contenuto dei testi_). M004, M054, M066, M096, M218, and M371 are some of the most frequent signs in Meriggi's \"syllabary\", all of which are attested in the above plot. M388, also found above, is speculated to function as a determinative marking personal names, similar to Sumerian 𒁹 _^m_ or 𒇽 _LU2_. Of possible note is the string M340 M054 M388, where this determinative occurs _after_ M054, a sign found in Meriggi's syllabary. In other cases M388 seems to preceed putative personal names, so it is interesting that this deviant usage should appear among the high-PMI trigrams."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3. Fuzzy $n$-gram Matching\n",
    "\n",
    "For large values of $n$, there may be very few $n$-grams which occur more than once. Fortunately, there may still be $n$-grams which are similar to one another. This cell computes the edit distance between pairs of $n$-grams and print out pairs which look similar. **This is best used with large values of $n$! May run slowly when $n$ is small.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching for similar 8-grams...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b864b5000c764e9999eff94cf177b9dd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value=''), IntProgress(value=0, max=55)))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "M124 M263 M048~D M314 M219 M048~D M240~E M057~B \t resembles\t M124 M263 M048~D M314 M218 M048~D M240~E M057~B\n",
      "\t M124 M263 M048~D M314 M219 M048~D M240~E M057~B occurs 1 times\n",
      "\t M124 M263 M048~D M314 M218 M048~D M240~E M057~B occurs 1 times\n"
     ]
    }
   ],
   "source": [
    "#################################################################\n",
    "# Edit this to change the length of n-grams being compared:\n",
    "N = 8\n",
    "#################################################################\n",
    "# Print out pairs which differ by at most THRESHOLD signs:\n",
    "THRESHOLD = 1\n",
    "\n",
    "print(\"Searching for similar %d-grams...\"%(N))\n",
    "\n",
    "# Computes edit distance between strings:\n",
    "def levenshtein_dist( ng1, ng2 ):\n",
    "    if len(ng1) == 0:\n",
    "        return len(ng2)\n",
    "    if len(ng2) == 0:\n",
    "        return len(ng1)\n",
    "    return min([\n",
    "        levenshtein_dist(ng1[1:],ng2) + 1,\n",
    "        levenshtein_dist(ng1,ng2[1:]) + 1,\n",
    "        levenshtein_dist(ng1[1:],ng2[1:]) + (1 if ng1[0] != ng2[0] else 0)\n",
    "    ])\n",
    "\n",
    "# Get ngrams\n",
    "ngrams = pype.ngrams( N, normalizeSigns = False, excludeNumeric=True, excludeHeader=False, excludeCorrections=True )\n",
    "for i,ngram1 in enumerate(util.log_progress(ngrams, every=1)):\n",
    "    for j,ngram2 in enumerate(ngrams):\n",
    "        if i >= j:\n",
    "            continue\n",
    "        if levenshtein_dist(ngram1, ngram2) <= THRESHOLD:\n",
    "            print(\" \".join(ngram1),\"\\t resembles\\t\",\" \".join(ngram2))\n",
    "            print(\"\\t\",\" \".join(ngram1),\"occurs\",ngrams[ngram1],\"times\")\n",
    "            print(\"\\t\",\" \".join(ngram2),\"occurs\",ngrams[ngram2],\"times\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although there are no 8-grams which occur more than once in the corpus, there is a pair of 8-grams which differ by only a single sign. The signs in question, M218 and M219, are visually similar: M218 is a diamond, and M219 is a diamond with a line through the middle. Both have appeared in lists of signs speculated to form part of personal names. Our fuzzy string matching reveals yet another commonality between the two signs, in that they both appear in identical contexts in a very long string of signs.\n",
    "\n",
    "We recommend adjusting `N` and `THRESHOLD` in the above code block to see other examples of similar strings. We believe that a complete study of the alternations in such strings will reveal novel and useful information about sign usage in proto-Elamite."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Hierarchical Sign Clustering\n",
    "\n",
    "In the vein of [Knight et al. (2011)](https://www.aclweb.org/anthology/W11-1202), we can cluster signs based on the contexts in which they appear. The resulting clusters will reveal sets of signs with similar distributions, which may imply some semantic or functional relation between those signs. Future work may build on these results by using neural word embeddings to compute similarity between terms, but the small size of the corpus may make training such a model impractical. For this reason we restrict ourselves to more traditional clustering techniques in this work.\n",
    "\n",
    "## 5.1. Neighbor-Based Clustering\n",
    "We begin by constructing a context vector counting how many times each other sign appears immediately to a given sign's left or right. The code for this is available in the `get_contexts` method of the `pe_toolkit_util` module. Using the context vectors returned by `get_contexts`, we use `scipy.cluster.hierarchy.linkage` to compute the similarity between signs and construct a hierarchical clustering based on the computed similarities. Finally, we can visualize the resulting clusters as a dendrogram, and even specify signs of interest to highlight. For ease of interpretation, the dendrogram is limited to signs which occur at least 50 times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "highlighting sign  M157\n",
      "highlighting sign  M157~A\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1987.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#################################################################\n",
    "# Edit this to change the number of signs in the dendrogram below:\n",
    "# The tree will contain all signs which appear at least THRESHOLD\n",
    "# times in the corpus:\n",
    "THRESHOLD = 50\n",
    "# If NORMALIZE is True, then sign variants are collapsed together\n",
    "# with their corresponding base sign. This may cause some signs\n",
    "# to display without pictures beside them, as there are no images\n",
    "# for some base signs.\n",
    "NORMALIZE = False\n",
    "\n",
    "from scipy.cluster.hierarchy import linkage, dendrogram\n",
    "from matplotlib.offsetbox import (TextArea, DrawingArea, OffsetImage,\n",
    "                                  AnnotationBbox)\n",
    "from PIL import Image\n",
    "import PIL.ImageOps\n",
    "\n",
    "# Compute context vectors\n",
    "labels, contexts = util.get_contexts( threshold=THRESHOLD, normalizeSigns=NORMALIZE )\n",
    "padded_labels_neighbor = [ \"                 %s\" % (''.join(labels[i]),) for i in range(len(labels)) ]\n",
    "\n",
    "# Cluster signs:\n",
    "clusters_neighbor = linkage(contexts, metric='cosine', method='complete')\n",
    "\n",
    "def draw_dendrogram(clusters, labels, highlight=[]):\n",
    "    clusters = [ [c[0],c[1],c[2]+0.05,c[3]] for c in clusters] # add a small offset to make the figure easier to read\n",
    "\n",
    "    plt.style.use('seaborn-white')\n",
    "\n",
    "    # Build the dendrogram:\n",
    "    plt.figure(figsize=(15, 0.6*len(labels)))\n",
    "\n",
    "    dendrogram(\n",
    "        clusters,\n",
    "        leaf_font_size=12.,\n",
    "        labels=labels,\n",
    "        orientation='right',\n",
    "        color_threshold=0.0,\n",
    "    )\n",
    "\n",
    "    # Color code symbols of interest to help find them in the diagram:\n",
    "    ax=plt.gca()\n",
    "    labels = ax.get_ymajorticklabels()\n",
    "    label_posns = ax.get_yticks()\n",
    "\n",
    "    for i,label in enumerate(labels):\n",
    "        draw_highlight = False\n",
    "        sign = label.get_text().strip(\" \")\n",
    "        if sign in highlight:\n",
    "            print(\"highlighting sign \",sign)\n",
    "            draw_highlight = True\n",
    "            label.set_color('red')\n",
    "        signimg = \"./pngs/PE_mainforms/\"+sign.upper().replace(\"~\",\"-\")+\".png\"\n",
    "        coords = (0,1,label_posns[i]+1,label_posns[i])\n",
    "\n",
    "        try:\n",
    "            arr_img = Image.open(signimg)\n",
    "            arr_img = arr_img.convert(\"RGBA\")\n",
    "\n",
    "            data = arr_img.getdata()\n",
    "\n",
    "            newData = []\n",
    "            for item in data:\n",
    "                if item[0] == 255 and item[1] == 255 and item[2] == 255:\n",
    "                    newData.append((255, 255, 255, 0))\n",
    "                else:\n",
    "                    newData.append(item)\n",
    "\n",
    "            arr_img.putdata(newData)\n",
    "\n",
    "\n",
    "            imagebox = OffsetImage(arr_img, zoom=0.45, interpolation='bicubic')\n",
    "            imagebox.image.axes = ax\n",
    "            xy = [0,1*label_posns[i]]\n",
    "            ab = AnnotationBbox(imagebox, xy,\n",
    "                                xybox=(-25. + (-9*(len(sign))),0.),\n",
    "                                xycoords='data',\n",
    "                                boxcoords=\"offset points\",\n",
    "                                pad=0,\n",
    "                                bboxprops=dict(edgecolor='red' if draw_highlight else 'none',alpha=0.5 if draw_highlight else 0)\n",
    "                                )\n",
    "            ax.add_artist(ab)\n",
    "        except FileNotFoundError:\n",
    "            pass\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "draw_dendrogram(clusters_neighbor, padded_labels_neighbor, highlight=[\"M157\", \"M157~A\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The shorter a branch is, the more similar the distribution of signs under that branch. For example, M157 and M157~A are joined by a very short branch, indicating that they are distributed very similarly across the corpus. This is a good benchmark to show that the clustering learns reasonable clusters, as M157 and M157~A are already understood to occur primarily at the beginning of tablets as \"header\" signs, and no meaningful difference between the two has yet been identified.\n",
    "\n",
    "This clustering also reveals some less expected results. M387~A and M387~EF are more similar to one another than to any other signs in the corpus, but they are clustered rather far from M387. This may indicate that these variants are semantically or functionally distinct from M387; further investigation of the context these signs appear in may help establish whether this is the case.\n",
    "\n",
    "Note that we can also compute dendrograms with sign variants collapsed together (so, for example, M157 and M157~A are treated as instances of the same sign). The structure of the dendrogram is broadly similar in this case, but individual signs do move around, and the differences are worth investigating in future work. Set `NORMALIZE` to `True` in the above code block to see the resulting dendrogram."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 HMM Clustering\n",
    "\n",
    "Although neighbor-based clustering was effective for Knight et al. (2011), proto-Elamite is written in a different script and it is possible that this script will respond better to alternative clustering techniques. Furthermore, by performing multiple clusterings with different techniques and looking for commonalities between them, we are able to state with greater certainty that the resulting clusters are meaningful and not simply the result of noise. Given the small size of the proto-Elamite corpus, we believe this kind of noise reduction is very likely to be useful.\n",
    "\n",
    "One alternative approach is to cluster signs based on the emission probabilities of a hidden Markov model (HMM). The intuition is that, if two signs tend to be emitted by the same state, they are more likely to be related than two signs which are never emitted by the same state.\n",
    "\n",
    "We begin by training an HMM on the corpus. This may take a few minutes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training HMM...\n",
      "Model converged? True\n"
     ]
    }
   ],
   "source": [
    "#################################################################\n",
    "# Edit these to change the HMM parameters:\n",
    "NUM_STATES = 10\n",
    "NUM_ITERS  = 75\n",
    "NORMALIZE=False\n",
    "# Seed to reproduce results in Born et al. 2019:\n",
    "SEED = 0xbeef\n",
    "\n",
    "from hmmlearn import hmm\n",
    "\n",
    "hmm_unigrams = pype.ngrams( 1, normalizeSigns=NORMALIZE, excludeHeader=False, excludeNumeric=False, excludeCorrections=True )\n",
    "sign_list = [sign for (sign,) in hmm_unigrams]\n",
    "sign_list.sort()\n",
    "sign_list.append( '<UNK>' )\n",
    "sign2i = {sign:i for i,sign in enumerate(sign_list)}    \n",
    "i2sign = {i:sign for i,sign in enumerate(sign_list)}\n",
    "\n",
    "tablets_with_real_content = [t for t in pype.tablets \n",
    "                             if re.sub( unreadable_signs_regex, \"\", \n",
    "                                       t.linearize( excludeNumeric=True ).upper() \n",
    "                                      ).strip() != '']\n",
    "\n",
    "# Convert corpus to a format hmmlearn will accept:\n",
    "unk_tokens = ['X', '[...]', '', '...']\n",
    "data = [ t.linearize( excludeNumeric = False ).upper().split() for t in tablets_with_real_content ]\n",
    "data = [ [re.sub( transliteration_annotations_regex, \"\", sign ) for sign in tablet] for tablet in data ]\n",
    "data = [ [sign if sign not in unk_tokens else '<UNK>' for sign in tablet] for tablet in data ]\n",
    "if NORMALIZE:\n",
    "    data = [ [ pype.normalize(sign) for sign in tablet ] for tablet in data ]\n",
    "data = [ np.array([[sign2i[sign.upper()]] for sign in tablet ]) for tablet in data ]\n",
    "\n",
    "# Concatenate tablets together to feed them to hmmlearn:\n",
    "X = np.concatenate(data)\n",
    "lengths = [len(tablet) for tablet in data]\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "# hmmlearn has a known issue where division-by-zero warnings occur\n",
    "# but the model still fits the data. We silence warnings for\n",
    "# this section to prevent them cluttering the interface.\n",
    "# See https://github.com/hmmlearn/hmmlearn/issues/146\n",
    "model = hmm.MultinomialHMM(n_components=NUM_STATES, n_iter=NUM_ITERS, random_state=SEED)\n",
    "print(\"Training HMM...\")\n",
    "model.fit(X, lengths)\n",
    "\n",
    "print(\"Model converged?\",model.monitor_.converged)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each sign, we retrieve the emission probability for that sign from every state in the HMM and compile these probabilities into a feature vector. We cluster signs based on these feature vectors using the same algorithm as in the context-based clustering:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "highlighting sign  M218\n",
      "highlighting sign  M096\n",
      "highlighting sign  M371\n",
      "highlighting sign  M057\n",
      "highlighting sign  M066\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1656 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1987.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#################################################################\n",
    "# Edit this to change the number of signs in the dendrogram below:\n",
    "# The tree will contain all signs which appear at least THRESHOLD\n",
    "# times in the corpus:\n",
    "THRESHOLD = 50\n",
    "\n",
    "# Create feature vectors for each sign using the\n",
    "# HMM emission probabilities:\n",
    "cluster_feats = []\n",
    "cluster_names = []\n",
    "\n",
    "for i, emission_probs in enumerate(model.emissionprob_.T):\n",
    "    normalized_probs = np.array([p/sum(emission_probs) for p in emission_probs])\n",
    "    if i2sign[i] != '<UNK>' and not i2sign[i].startswith('N') and hmm_unigrams[(i2sign[i],)] >= THRESHOLD and '+' not in i2sign[i]:\n",
    "        cluster_names.append( i2sign[i] )\n",
    "        cluster_feats.append( normalized_probs )\n",
    "cluster_feats = np.array( cluster_feats )\n",
    "\n",
    "\n",
    "# Create dendogram:\n",
    "plt.figure(figsize=(15, len(cluster_names)/2))\n",
    "clusters_hmm = linkage(cluster_feats, metric='cosine', method='complete')\n",
    "clusters_hmm = [ [c[0],c[1],c[2]+0.01,c[3]] for c in clusters_hmm] # Add a small offset to make the figure easier to read\n",
    "padded_labels_hmm = [ \"                 %s\" % (''.join(cluster_names[i]),) for i in range(len(cluster_names)) ]\n",
    "\n",
    "draw_dendrogram(clusters_hmm, padded_labels_hmm, highlight=[\"M057\", \"M066\", \"M096\", \"M218\", \"M371\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At a glance, some similarities to the context-based clustering are apparent. M157 and M157~A again form a tight cluster, as expected, and M387~A and ~EF still cluster together separate from M387. There are even some very large subtrees in common with the neighbor-based dendrogram. M057, M066, M096, M218, and M371 all cluster close together here and in the neighbor-based approach; these signs were previously introduced in the discussion of PMI, where we noted that they are speculated to form parts of personal names.\n",
    "\n",
    "The similarities between this and the neighbor-based clustering are encouraging, and suggest that there are strong signals in the data which both methods are able to pick up on despite the small corpus size.\n",
    "\n",
    "We will introduce one more clustering before moving on to a more quantitative analysis of the similarities between these trees."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3. Brown Clustering\n",
    "\n",
    "As a third clustering technique, we performed Brown clustering using the generalized-brown implementation found here: https://github.com/sean-chester/generalised-brown\n",
    "\n",
    "`saved_models/brown.240.120` contains the 120 clusters returned by this algorithm using an active set size of 240. The following code block loads these clusters and converts them into a dendrogram suitable for comparison with the previous clustering methods. We do not provide a way to run the clustering interactively in this notebook, as parts of the clustering implementation are written in C++."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1944 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusters_brown, padded_labels_brown = util.load_brown_model( \"saved_models/brown.240.120\" )\n",
    "# To see the clustering learned when sign variants are collapsed, uncomment this line:\n",
    "# clusters_brown, padded_labels_brown = util.load_brown_model( \"saved_models/brown.240.120.collapsed\" )\n",
    "draw_dendrogram( clusters_brown, padded_labels_brown, highlight=[] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again, visual inspection reveals broad similarities to the preceding techniques. The following section attempts to formalize this intuition by looking for the most stable clusters across all three techniques."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.4. Stable Clusters\n",
    "\n",
    "Given the small size of the proto-Elamite corpus, it is possible that some clusters may result from noise in the data rather than legitimate similarities between signs. By looking for groupings which recur across all three clustering methods, we hope to maximize the likelihood of finding meaningful sign clusters.\n",
    "\n",
    "We identify stable clusters using the following heuristic: given a set of signs $S$, we compute for each clustering the height of the smallest subtree containing every sign in $S$. If this height is small across every clustering (which we take to mean no greater than $2|S|$), then the signs in $S$ are consistently grouped closely together, and we call $S$ a stable cluster.\n",
    "\n",
    "The following code searches for stable clusters. The height computed as our heuristic is printed out along with the clusters. Recall that a binary tree with  $|S|$ leaves has a height between $\\lceil\\log_2 |S|\\rceil$ and $|S|-1$. Thus if $S$ is a stable cluster with height $\\lceil\\log_2 |S|\\rceil$, the signs in $S$ always group together as tightly as possible, with no intervening signs in any of the clusterings. By contrast, if $S$ has height greater than $|S|-1$, then at least one of the clusterings groups some intervening signs in with the set $S$.\n",
    "\n",
    "By default, this code block is limited to finding clusters of at most 3 signs. You can increase the `MAX_CLUSTER_SIZE` parameter to display larger groupings, but be advised that this becomes increasingly slow as `MAX_CLUSTER_SIZE` grows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching for repeated clusters of 2 signs...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "effe5aec65ba4ec9a321e0dee263ac2b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value=''), IntProgress(value=0, max=1035)))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 16 stable clusters:\n",
      "\tHeight: 1 \tSigns: M003~B, M263~A\n",
      "\tHeight: 1 \tSigns: M157, M157~A\n",
      "\tHeight: 1 \tSigns: M387~A, M387~EF\n",
      "\tHeight: 2 \tSigns: M057, M066\n",
      "\tHeight: 2 \tSigns: M059, M365\n",
      "\tHeight: 2 \tSigns: M297, M297~B\n",
      "\tHeight: 2 \tSigns: M377, M387\n",
      "\tHeight: 3 \tSigns: M004, M377~E\n",
      "\tHeight: 3 \tSigns: M005~A, M305\n",
      "\tHeight: 4 \tSigns: M001, M054\n",
      "\tHeight: 4 \tSigns: M004, M219\n",
      "\tHeight: 4 \tSigns: M057, M096\n",
      "\tHeight: 4 \tSigns: M066, M096\n",
      "\tHeight: 4 \tSigns: M218, M371\n",
      "\tHeight: 4 \tSigns: M219, M377~E\n",
      "\tHeight: 4 \tSigns: M288, M354\n",
      "\n",
      "Searching for repeated clusters of 3 signs...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "316e39cedfcb408a91434f63f4259a1f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value=''), IntProgress(value=0, max=15180)))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 100 stable clusters:\n",
      "\tHeight: 4 \tSigns: M004, M219, M377~E\n",
      "\tHeight: 4 \tSigns: M057, M066, M096\n",
      "\tHeight: 5 \tSigns: M005~A, M305, M387~C\n",
      "\tHeight: 5 \tSigns: M057, M066, M218\n",
      "\tHeight: 5 \tSigns: M057, M066, M371\n",
      "\tHeight: 5 \tSigns: M057, M096, M218\n",
      "\tHeight: 5 \tSigns: M057, M096, M371\n",
      "\tHeight: 5 \tSigns: M057, M218, M371\n",
      "\tHeight: 5 \tSigns: M066, M096, M218\n",
      "\tHeight: 5 \tSigns: M066, M096, M371\n",
      "\tHeight: 5 \tSigns: M066, M218, M371\n",
      "\tHeight: 5 \tSigns: M096, M218, M371\n",
      "\tHeight: 5 \tSigns: M377, M386~A, M387\n",
      "\tHeight: 6 \tSigns: M003~B, M263, M263~A\n",
      "\tHeight: 6 \tSigns: M004, M219, M259\n",
      "\tHeight: 6 \tSigns: M004, M219, M377\n",
      "\tHeight: 6 \tSigns: M004, M219, M386~A\n",
      "\tHeight: 6 \tSigns: M004, M219, M387\n",
      "\tHeight: 6 \tSigns: M004, M219, M387~A\n",
      "\tHeight: 6 \tSigns: M004, M219, M387~EF\n",
      "\tHeight: 6 \tSigns: M004, M259, M377\n",
      "\tHeight: 6 \tSigns: M004, M259, M377~E\n",
      "\tHeight: 6 \tSigns: M004, M259, M386~A\n",
      "\tHeight: 6 \tSigns: M004, M259, M387\n",
      "\tHeight: 6 \tSigns: M004, M259, M387~A\n",
      "\tHeight: 6 \tSigns: M004, M259, M387~EF\n",
      "\tHeight: 6 \tSigns: M004, M377, M377~E\n",
      "\tHeight: 6 \tSigns: M004, M377, M386~A\n",
      "\tHeight: 6 \tSigns: M004, M377, M387\n",
      "\tHeight: 6 \tSigns: M004, M377, M387~A\n",
      "\tHeight: 6 \tSigns: M004, M377, M387~EF\n",
      "\tHeight: 6 \tSigns: M004, M377~E, M386~A\n",
      "\tHeight: 6 \tSigns: M004, M377~E, M387\n",
      "\tHeight: 6 \tSigns: M004, M377~E, M387~A\n",
      "\tHeight: 6 \tSigns: M004, M377~E, M387~EF\n",
      "\tHeight: 6 \tSigns: M004, M386~A, M387\n",
      "\tHeight: 6 \tSigns: M004, M386~A, M387~A\n",
      "\tHeight: 6 \tSigns: M004, M386~A, M387~EF\n",
      "\tHeight: 6 \tSigns: M004, M387, M387~A\n",
      "\tHeight: 6 \tSigns: M004, M387, M387~EF\n",
      "\tHeight: 6 \tSigns: M004, M387~A, M387~EF\n",
      "\tHeight: 6 \tSigns: M072, M346, M367\n",
      "\tHeight: 6 \tSigns: M072, M346, M376\n",
      "\tHeight: 6 \tSigns: M072, M367, M376\n",
      "\tHeight: 6 \tSigns: M219, M259, M377\n",
      "\tHeight: 6 \tSigns: M219, M259, M377~E\n",
      "\tHeight: 6 \tSigns: M219, M259, M386~A\n",
      "\tHeight: 6 \tSigns: M219, M259, M387\n",
      "\tHeight: 6 \tSigns: M219, M259, M387~A\n",
      "\tHeight: 6 \tSigns: M219, M259, M387~EF\n",
      "\tHeight: 6 \tSigns: M219, M377, M377~E\n",
      "\tHeight: 6 \tSigns: M219, M377, M386~A\n",
      "\tHeight: 6 \tSigns: M219, M377, M387\n",
      "\tHeight: 6 \tSigns: M219, M377, M387~A\n",
      "\tHeight: 6 \tSigns: M219, M377, M387~EF\n",
      "\tHeight: 6 \tSigns: M219, M377~E, M386~A\n",
      "\tHeight: 6 \tSigns: M219, M377~E, M387\n",
      "\tHeight: 6 \tSigns: M219, M377~E, M387~A\n",
      "\tHeight: 6 \tSigns: M219, M377~E, M387~EF\n",
      "\tHeight: 6 \tSigns: M219, M386~A, M387\n",
      "\tHeight: 6 \tSigns: M219, M386~A, M387~A\n",
      "\tHeight: 6 \tSigns: M219, M386~A, M387~EF\n",
      "\tHeight: 6 \tSigns: M219, M387, M387~A\n",
      "\tHeight: 6 \tSigns: M219, M387, M387~EF\n",
      "\tHeight: 6 \tSigns: M219, M387~A, M387~EF\n",
      "\tHeight: 6 \tSigns: M259, M377, M377~E\n",
      "\tHeight: 6 \tSigns: M259, M377, M386~A\n",
      "\tHeight: 6 \tSigns: M259, M377, M387\n",
      "\tHeight: 6 \tSigns: M259, M377, M387~A\n",
      "\tHeight: 6 \tSigns: M259, M377, M387~EF\n",
      "\tHeight: 6 \tSigns: M259, M377~E, M386~A\n",
      "\tHeight: 6 \tSigns: M259, M377~E, M387\n",
      "\tHeight: 6 \tSigns: M259, M377~E, M387~A\n",
      "\tHeight: 6 \tSigns: M259, M377~E, M387~EF\n",
      "\tHeight: 6 \tSigns: M259, M386~A, M387\n",
      "\tHeight: 6 \tSigns: M259, M386~A, M387~A\n",
      "\tHeight: 6 \tSigns: M259, M386~A, M387~EF\n",
      "\tHeight: 6 \tSigns: M259, M387, M387~A\n",
      "\tHeight: 6 \tSigns: M259, M387, M387~EF\n",
      "\tHeight: 6 \tSigns: M259, M387~A, M387~EF\n",
      "\tHeight: 6 \tSigns: M346, M367, M376\n",
      "\tHeight: 6 \tSigns: M377, M377~E, M386~A\n",
      "\tHeight: 6 \tSigns: M377, M377~E, M387\n",
      "\tHeight: 6 \tSigns: M377, M377~E, M387~A\n",
      "\tHeight: 6 \tSigns: M377, M377~E, M387~EF\n",
      "\tHeight: 6 \tSigns: M377, M386~A, M387~A\n",
      "\tHeight: 6 \tSigns: M377, M386~A, M387~EF\n",
      "\tHeight: 6 \tSigns: M377, M387, M387~A\n",
      "\tHeight: 6 \tSigns: M377, M387, M387~EF\n",
      "\tHeight: 6 \tSigns: M377, M387~A, M387~EF\n",
      "\tHeight: 6 \tSigns: M377~E, M386~A, M387\n",
      "\tHeight: 6 \tSigns: M377~E, M386~A, M387~A\n",
      "\tHeight: 6 \tSigns: M377~E, M386~A, M387~EF\n",
      "\tHeight: 6 \tSigns: M377~E, M387, M387~A\n",
      "\tHeight: 6 \tSigns: M377~E, M387, M387~EF\n",
      "\tHeight: 6 \tSigns: M377~E, M387~A, M387~EF\n",
      "\tHeight: 6 \tSigns: M386~A, M387, M387~A\n",
      "\tHeight: 6 \tSigns: M386~A, M387, M387~EF\n",
      "\tHeight: 6 \tSigns: M386~A, M387~A, M387~EF\n",
      "\tHeight: 6 \tSigns: M387, M387~A, M387~EF\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#################################################################\n",
    "# Edit this to change when the algorithm stops looking for \n",
    "# larger repeated clusters. May be slow for values above 3.\n",
    "MAX_CLUSTER_SIZE = 3\n",
    "# Increase this to skip small clusters.\n",
    "MIN_CLUSTER_SIZE = 2\n",
    "\n",
    "# Helper to convert a scipy linkage array\n",
    "# back into a tree (represented as a nested list):\n",
    "def get_cluster(linkage, labels, cluster_num):\n",
    "    n = len(linkage)+1\n",
    "    if cluster_num < n:\n",
    "        return labels[int(cluster_num)].strip()\n",
    "    index = int(cluster_num - n) \n",
    "    row = linkage[index]\n",
    "    c1 = row[0]\n",
    "    c2 = row[1]\n",
    "    return [get_cluster(linkage,labels,c1), get_cluster(linkage,labels,c2)]\n",
    "\n",
    "# Get a representation of each clustering:\n",
    "all_clusters_brown,_ = util.build_brown_tree( \"saved_models/brown.240.120\" )\n",
    "all_clusters_hmm = get_cluster(clusters_hmm, padded_labels_hmm, len(clusters_hmm) + len(padded_labels_hmm) - 1)\n",
    "all_clusters_neighbor = get_cluster(clusters_neighbor,padded_labels_neighbor, len(clusters_neighbor) + len(padded_labels_neighbor) - 1)\n",
    "\n",
    "# Helper to flatten a nested list:\n",
    "def flatten(lst):\n",
    "    if lst == []:\n",
    "        return lst\n",
    "    elif not isinstance(lst,list):\n",
    "        return [lst]\n",
    "    else:\n",
    "        return flatten(lst[0]) + flatten(lst[1:])\n",
    "\n",
    "# Heuristic to compute closeness of a set of signs \n",
    "# in a dendrogram:\n",
    "def height_of_lowest_subtree_containing(t,signs):\n",
    "    def height(lst):\n",
    "        if not isinstance(lst,list):\n",
    "            return 0\n",
    "        return 1+max([height(lst[0]),height(lst[1])])\n",
    "    subtrees = [st for st in util.enum_subtrees(t) if all(s in flatten(st) for s in signs)]\n",
    "    return height(max(subtrees,key=lambda x:-height(x)))\n",
    "\n",
    "from itertools import combinations\n",
    "import math\n",
    "\n",
    "all_pairwise_dists = []\n",
    "for set_size in range(MIN_CLUSTER_SIZE,MAX_CLUSTER_SIZE + 1):\n",
    "    \n",
    "    print(\"Searching for repeated clusters of %d signs...\"%(set_size))\n",
    "    min_height = math.ceil(math.log(set_size,2))\n",
    "    dists = []\n",
    "    num_combinations = math.factorial(len(padded_labels_neighbor)) / (math.factorial(len(padded_labels_neighbor)-set_size) * math.factorial(set_size))\n",
    "    for subset in util.log_progress(combinations(padded_labels_neighbor, set_size), every=100, size=num_combinations):\n",
    "        subset = [sign.strip() for sign in subset]\n",
    "        try:\n",
    "            dists.append((subset,max([\n",
    "                height_of_lowest_subtree_containing(all_clusters_hmm,subset),\n",
    "                height_of_lowest_subtree_containing(all_clusters_neighbor,subset),\n",
    "                height_of_lowest_subtree_containing(all_clusters_brown,subset)\n",
    "            ])))\n",
    "        except ValueError:\n",
    "            # If there is no subtree containing all the signs in all three trees,\n",
    "            # take no action.\n",
    "            pass\n",
    "    all_pairwise_dists.append( (set_size, dists) )\n",
    "    \n",
    "    # height_threshold determines how tall a subtree can be before\n",
    "    # the signs within are no longer considered \"close\" to one another.\n",
    "    height_threshold = 2*set_size\n",
    "    best_clusters = sorted([d for d in dists if d[1] <= height_threshold],key=lambda x:x[1])\n",
    "    \n",
    "    print(\"Found %d stable clusters:\"%(len(best_clusters)))\n",
    "    for signs, dist in best_clusters:\n",
    "        print(\"\\tHeight:\",dist,\"\\tSigns:\",\", \".join(signs))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This analysis confirms the intuitions from the preceding sections: all three methods group M157 with M157~A and M387~A with M387~EF. An additional group, M003~B and M263~A, also recurs across every technique, and is discussed in Born et al. 2019. \n",
    "\n",
    "A limitation of our heuristic is that it finds a large number of overlapping clusters; for example, see all of the clusters containing M057, M066, M096, M218, and M371. This indicates that these are subsets of an even larger stable cluster containing all of these signs. This cluster, which appears to comprise signs used in possible personal names, is also discussed at some length in Born et al. 2019. Notably, M057, M066, and M096 form a tighter group within this larger cluster, as indicated by their lower height in the printout above. It remains for future work to investigate whether this is a meaningful subgroup: perhaps these signs are used ideographically while the other signs in the cluster are syllabic, or vice versa?\n",
    "\n",
    "Contrary to our intuitive analysis in the preceding sections, M387, M387~A, and M387~EF do form a stable cluster; however, the height of this cluster is 6, indicating that a large number of signs may intervene between M387 and its variants. This may indicate that our threshold for calling a cluster stable is too generous, and we should restrict our analysis to clusters with smaller heights. Alternatively, it may indicate that these signs belong together, but noise in the data causes other unrelated material to intervene between them. Born et al. 2019 focusses its discussion on clusters with small heights, and leaves the interpretation of these broader clusters to future work.\n",
    "\n",
    "Other small stable clusters which have not yet been discussed include:\n",
    "- M005~A, M305, and M387~C\n",
    "- M004 with a variety of other signs (M219, M259, M377...), all belonging to one of Meriggi, Desset, or Dahl's \"syllabaries\"; these may form another syllabic cluster separate from M057, M066, M096, M218, and M317\n",
    "- M219 and M377~E\n",
    "- M377, M386~A, and M387, all of which are either numeric signs (used in a non-numeric context) or clearly derived from numeric signs\n",
    "\n",
    "We believe that future decipherment work should more closely examine these groups, as the signs in these clusters clearly share some semantic or functional relationship which is strong enough to be detected by multiple distinct clustering techniques. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# 6. LDA\n",
    "\n",
    "Latent Dirichlet Allocation (LDA) is a topic modeling algorithm which attempts to group related words into topics and determine which topics are discussed in a given set of documents. Notably, LDA infers topical relationships solely based on rates of term co-occurrence, meaning it can run on undeciphered texts to yield information on which terms may be related. LDA differs from the preceding clusterings in that it also provides a means for grouping tablets based on the topics they discuss, which may reveal genres or other meaningful divisions of the corpus.\n",
    "\n",
    "Note that the degree of semantic relatedness between words in a topic may vary widely. In English, \"kitten\", \"cat\", and \"milk\" may all belong to a topic of cat-related words; although \"kitten\" and \"cat\" are instances of the same class of animal, \"kitten\" and \"milk\" are not. Thus we must be careful when interpreting topics from an undeciphered corpus like proto-Elamite: although an undeciphered sign may belong to a topic with other known human or animal signs, this does not necessarily entail that the undeciphered sign is also a human or animal.\n",
    "\n",
    "The following code block trains an LDA topic model using the `gensim` package. By default the algorithm will learn 10 topics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trained LDA topic model.\n",
      "Coherence:  0.4855323176945183\n",
      "Perplexity:  -5.939734601950598\n"
     ]
    }
   ],
   "source": [
    "#################################################################\n",
    "# Topic model parameters:\n",
    "NUM_TOPICS = 10\n",
    "CHUNKSIZE = 500 \n",
    "PASSES = 50\n",
    "ITERATIONS = 5000\n",
    "EVAL_EVERY=1\n",
    "# Seed to reproduce model used in Born et al. 2019:\n",
    "SEED=3628606606\n",
    "\n",
    "from gensim.models.coherencemodel import CoherenceModel\n",
    "from gensim.models.ldamodel import LdaModel\n",
    "from gensim.corpora.dictionary import Dictionary\n",
    "from gensim.models import Phrases\n",
    "\n",
    "tablets_with_real_content = [t for t in pype.tablets \n",
    "                             if re.sub( unreadable_signs_regex, \"\", \n",
    "                                       t.linearize( excludeNumeric=True ).upper() \n",
    "                                      ).strip() != '']\n",
    "\n",
    "unk_tokens = ['X', '[...]', '', '...']\n",
    "data = [ t.linearize( excludeNumeric = True ).upper().split() for t in tablets_with_real_content ]\n",
    "data = [ [re.sub( r\"\\?|#|\\[|\\]|!\", \"\", sign ) for sign in tablet] for tablet in data ]\n",
    "data = [ [sign for sign in tablet if sign not in unk_tokens] for tablet in data ]\n",
    "\n",
    "import warnings\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter('ignore')\n",
    "    \n",
    "    bigram = Phrases(data, min_count=10) \n",
    "    # min_count=10 seems to be fairly standard.\n",
    "    # Due to corpus size might want to be more\n",
    "    # forgiving and reduce min_count a little?\n",
    "    trigram = Phrases(bigram[data])\n",
    "\n",
    "    for i in range(len(data)):\n",
    "        # Add bigram and trigram features to each document: \n",
    "        data[i] = np.append(data[i], [token for token in bigram[data[i]] if '_' in token])\n",
    "        data[i] = np.append(data[i], [token for token in trigram[data[i]] if '_' in token])\n",
    "\n",
    "# Convert documents to bags of words:\n",
    "dictionary = Dictionary(data)\n",
    "_ = dictionary[0] # id2token is lazy-loaded. Do this to get it in memory.\n",
    "corpus = [dictionary.doc2bow(doc) for doc in data]\n",
    "id2word = dictionary.id2token\n",
    "\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter('ignore')\n",
    "\n",
    "    # Train LDA model:\n",
    "    lda_model=LdaModel(\n",
    "        corpus=corpus, \\\n",
    "        id2word=dictionary, \\\n",
    "        num_topics=NUM_TOPICS, \\\n",
    "        chunksize=CHUNKSIZE, \\\n",
    "        passes=PASSES, \\\n",
    "        iterations=ITERATIONS, \\\n",
    "        eval_every=EVAL_EVERY, \\\n",
    "        random_state=SEED\n",
    "    )\n",
    "    coherencemodel = CoherenceModel(\n",
    "        model=lda_model, \\\n",
    "        texts=data, \\\n",
    "        dictionary=dictionary, \\\n",
    "        coherence='c_v')\n",
    "\n",
    "    print(\"Trained LDA topic model.\")\n",
    "    print(\"Coherence: \",coherencemodel.get_coherence())\n",
    "    print(\"Perplexity: \",lda_model.log_perplexity(corpus))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1 LDA Model Visualization\n",
    "\n",
    "This cell will produce a visualization of the resulting topic model using [pyLDAvis](https://github.com/bmabey/pyLDAvis). The size of a circle represents the prominence of that topic in the proto-Elamite corpus. The distance between two circles is an approximate measure of the similarity between the corresponding topics. The histogram to the right shows term frequencies; hover over a topic to see the term frequencies within that topic, or hover over a term to see its distribution across the topics.\n",
    "\n",
    "**This visualization is not available when viewing the static notebook on github: run the notebook locally, on nbviewer, or on Binder to access it.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el226021407084171200805093573634\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el226021407084171200805093573634_data = {\"mdsDat\": {\"x\": [-0.0692396322191677, -0.023029186505464337, -0.048604312478054745, -0.17436092711523893, 0.15843288115055051, 0.2799210178212482, -0.21059511684653212, -0.07680487198748084, -0.014328257577673179, 0.1786084057578128], \"y\": [0.03826507476545914, 0.20122007671984118, -0.1759289762994357, 0.07486793128427066, -0.007177508643556309, -0.056460522838841856, 0.0186686642123622, -0.0012137884059772847, -0.2541843666295582, 0.16194341583543723], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [5.302454948425293, 8.53784465789795, 4.213594436645508, 7.071534633636475, 10.365279197692871, 27.52533531188965, 5.419681072235107, 4.288581371307373, 13.057682037353516, 14.218015670776367]}, \"tinfo\": {\"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", 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\"M302~I\", \"|M153+M377~E+M153|\", \"M063~A\", \"M384~E\", \"M207~D\", \"M149~H\", \"M135\", \"M268~B\", \"M268~C\", \"M210~D\", \"M073~B\", \"M153\", \"M228~GA\", \"M080~B\", \"M317~A\", \"M057~A2\", \"M296\", \"M157\", \"M009\", \"M327\", \"M365\", \"M388\", \"M081\", \"M263\", \"M036\", \"M354\", \"M263~A\", \"M263~B1\", \"M002\", \"M266~B\", \"M122\", \"M265~F\", \"M147\", \"|M036+1(N30C)|\", \"M384~D\", \"M050~K\", \"M069~A\", \"M263~B\", \"|M036+1(N24)|\", \"M387_M069~A\", \"M316~E2\", \"M264~B\", \"|M351+X|\", \"M038~E\", \"|M175+M136|\", \"M277\", \"M323~I\", \"M263~E\", \"M379~C\", \"M128~D\", \"M309\", \"M264~A1\", \"M252~Q\", \"M347_M377~E\", \"|M036+1(N30D)|\", \"M243~J\", \"M111~A\", \"M323\", \"M051~B\", \"M387\", \"M387~EF\", \"M081\", \"M387~A\", \"M010~2\", \"M297\", \"M033\", \"M263\", \"M001\", \"M305\", \"M157~A\", \"M264~A\", \"M054\", \"M157\", \"M260\", \"M297~B\", \"M370\", \"M072\", \"M373\", \"M046\", \"M387~I\", \"|M370~B+M072|\", \"|M370~B+M388|\", \"M051~C\", \"M010\", \"M346~B\", \"|M370+M072+M370|\", \"M051~A\", \"M072_M046\", \"M269~A1\", \"|M370+M388+M370|\", \"M041~G\", \"M136~B\", \"M373~A\", \"M003~B\", \"M246~B\", \"M312\", \"M318~A1\", \"M387~I_M387~I\", \"M158~H\", \"|M370+M046+M370|\", \"M288~D\", \"M010~1\", \"M016\", \"M352~C\", \"M010~6\", \"M310\", \"M124\", \"M054\", \"M203~A\", \"M288\", \"M369\", \"M376\", \"M371\", \"M388\", \"|M351+1(N14)|\", \"|M305+X|\", \"M001\", \"M009\", \"M005~A\", \"M380~B\", \"M218\", \"M305\"], \"Total\": [723.0, 222.0, 191.0, 579.0, 482.0, 264.0, 248.0, 243.0, 202.0, 202.0, 291.0, 220.0, 232.0, 111.0, 110.0, 100.0, 106.0, 201.0, 71.0, 92.0, 86.0, 91.0, 54.0, 46.0, 90.0, 43.0, 163.0, 113.0, 58.0, 59.0, 54.88302230834961, 6.315791130065918, 6.1983466148376465, 6.0677409172058105, 5.745706081390381, 5.480724811553955, 5.364043235778809, 5.246391296386719, 5.232809543609619, 4.910463333129883, 4.910037517547607, 4.574887752532959, 4.529354095458984, 4.5146636962890625, 4.411859512329102, 4.193011283874512, 4.146378040313721, 4.030615329742432, 3.9131250381469727, 3.807368516921997, 191.84494018554688, 3.429020881652832, 21.86293601989746, 3.358142614364624, 3.358142614364624, 3.358142614364624, 3.3580315113067627, 3.3580307960510254, 12.96753215789795, 3.3116486072540283, 37.2330207824707, 9.291571617126465, 8.167937278747559, 29.78469467163086, 5.418014049530029, 15.229183197021484, 100.64836883544922, 48.23405838012695, 33.244537353515625, 9.716060638427734, 7.219103813171387, 22.18584442138672, 27.766996383666992, 20.24532699584961, 28.074190139770508, 201.02052307128906, 39.77939224243164, 29.190536499023438, 89.34529113769531, 130.13299560546875, 19.336925506591797, 13.1414794921875, 10.840553283691406, 10.725495338439941, 10.6831693649292, 10.07635498046875, 27.29231834411621, 9.632177352905273, 9.43649959564209, 7.991181373596191, 7.875331878662109, 7.20798397064209, 7.080695629119873, 7.025670051574707, 6.385557651519775, 6.1731486320495605, 6.07393217086792, 5.635918140411377, 5.634648323059082, 4.896368026733398, 9.920413970947266, 4.625507831573486, 28.835468292236328, 4.157680988311768, 4.044211387634277, 3.7180440425872803, 6.9114837646484375, 3.6057639122009277, 3.605708360671997, 3.5976498126983643, 723.941650390625, 6.114050388336182, 13.311269760131836, 46.47138977050781, 17.425983428955078, 26.867393493652344, 16.580650329589844, 86.80049896240234, 201.02052307128906, 31.087038040161133, 64.42032623291016, 89.34529113769531, 48.23405838012695, 46.05876922607422, 26.235280990600586, 40.93702697753906, 33.244537353515625, 37.86151885986328, 11.92866325378418, 9.425431251525879, 9.345009803771973, 9.19347095489502, 7.60698938369751, 6.937542915344238, 6.652702808380127, 6.6521430015563965, 5.98162317276001, 5.464723110198975, 5.422157287597656, 9.769972801208496, 4.81862735748291, 4.643248558044434, 3.9733428955078125, 3.9310965538024902, 3.864546537399292, 3.8635833263397217, 3.8217616081237793, 3.712911367416382, 3.3988077640533447, 3.398111581802368, 29.42925453186035, 3.1946794986724854, 3.1946117877960205, 3.1946117877960205, 3.194430112838745, 3.151357412338257, 15.136739730834961, 9.229313850402832, 71.83399963378906, 30.919923782348633, 15.37409782409668, 15.80265998840332, 37.86151885986328, 49.375057220458984, 243.610107421875, 201.02052307128906, 91.45791625976562, 89.34529113769531, 26.78939437866211, 11.33865737915039, 163.27157592773438, 90.49833679199219, 28.171344757080078, 222.34579467773438, 23.03827476501465, 18.5314998626709, 14.476646423339844, 10.769888877868652, 9.39466381072998, 8.92890453338623, 7.602966785430908, 7.574001312255859, 7.195553779602051, 6.864518165588379, 6.383591175079346, 6.23126745223999, 6.2310614585876465, 5.931976795196533, 12.377630233764648, 5.717887878417969, 5.537976264953613, 5.357552528381348, 5.14630651473999, 4.753233909606934, 4.753106594085693, 4.631682872772217, 4.618744373321533, 4.361191272735596, 4.360342502593994, 4.287578582763672, 4.161369323730469, 4.1204657554626465, 3.9998531341552734, 86.80049896240234, 17.970428466796875, 12.303335189819336, 40.93702697753906, 27.073881149291992, 64.42032623291016, 16.210390090942383, 52.048274993896484, 130.13299560546875, 202.94290161132812, 39.77939224243164, 10.5485200881958, 29.444862365722656, 46.47138977050781, 11.200858116149902, 113.45681762695312, 31.486465454101562, 22.651554107666016, 18.988956451416016, 18.6423397064209, 18.413402557373047, 12.106986045837402, 25.877592086791992, 11.56662368774414, 11.539292335510254, 11.077760696411133, 10.97045612335205, 9.011417388916016, 8.996567726135254, 8.938458442687988, 21.367246627807617, 9.282815933227539, 7.5174078941345215, 7.083432197570801, 6.767617225646973, 6.575371742248535, 6.1849164962768555, 5.915231227874756, 5.828032970428467, 42.45903396606445, 4.869830131530762, 4.482362270355225, 33.26433181762695, 4.258199691772461, 28.108896255493164, 4.212113380432129, 4.13748836517334, 21.686275482177734, 19.690290451049805, 53.76102828979492, 113.45681762695312, 11.97648811340332, 579.6676025390625, 201.02052307128906, 243.610107421875, 14.31829833984375, 46.05876922607422, 89.34529113769531, 248.1008758544922, 36.96440124511719, 100.64836883544922, 723.941650390625, 482.9601135253906, 15.601045608520508, 130.13299560546875, 220.0768585205078, 202.5222625732422, 67.98028564453125, 66.77021026611328, 39.23707580566406, 38.58435821533203, 35.66384506225586, 32.11963653564453, 27.74384880065918, 26.710155487060547, 26.041101455688477, 19.117719650268555, 232.86692810058594, 18.545766830444336, 17.82833480834961, 17.63566017150879, 16.55243682861328, 58.04794692993164, 14.676824569702148, 13.974855422973633, 110.96996307373047, 482.9601135253906, 52.28977584838867, 11.22083568572998, 11.062397956848145, 11.044150352478027, 10.966337203979492, 10.950681686401367, 41.786376953125, 10.059630393981934, 9.992733001708984, 43.64960479736328, 80.63076782226562, 90.511474609375, 41.21286392211914, 84.95912170410156, 291.72406005859375, 34.77471160888672, 33.28514099121094, 37.18898010253906, 42.190208435058594, 579.6676025390625, 47.66188049316406, 163.27157592773438, 36.962276458740234, 42.82801818847656, 202.94290161132812, 264.621826171875, 220.0768585205078, 243.610107421875, 49.80872344970703, 49.375057220458984, 43.765316009521484, 32.971031188964844, 31.51108169555664, 30.471403121948242, 29.126996994018555, 26.354690551757812, 25.818620681762695, 21.58657455444336, 19.311735153198242, 17.112293243408203, 30.84981346130371, 13.63971996307373, 11.564908027648926, 11.255661964416504, 11.08993148803711, 11.08993148803711, 11.08993148803711, 11.08993148803711, 9.24169921875, 7.3485798835754395, 6.745980262756348, 6.6328816413879395, 6.311233997344971, 6.310863971710205, 32.89863204956055, 5.888123989105225, 4.964338779449463, 4.654676914215088, 4.654499530792236, 4.103373050689697, 29.444862365722656, 16.99022102355957, 34.89019012451172, 202.94290161132812, 89.34529113769531, 46.561485290527344, 10.342416763305664, 10.01361083984375, 27.310420989990234, 9.084094047546387, 8.179610252380371, 25.411630630493164, 7.723353385925293, 7.294956684112549, 7.0804901123046875, 6.07582426071167, 5.9475417137146, 5.8175835609436035, 4.659183025360107, 8.803932189941406, 16.373180389404297, 4.1863627433776855, 4.07232141494751, 3.743898630142212, 3.514946699142456, 7.248042106628418, 3.3711674213409424, 3.3010973930358887, 3.2567665576934814, 3.255657196044922, 3.0286006927490234, 3.0286006927490234, 3.0286006927490234, 3.028597354888916, 3.028597354888916, 9.133554458618164, 11.33865737915039, 52.048274993896484, 11.38827133178711, 5.822035312652588, 8.289648056030273, 7.599271297454834, 26.78939437866211, 201.02052307128906, 202.94290161132812, 31.486465454101562, 48.23405838012695, 579.6676025390625, 38.49692153930664, 163.27157592773438, 111.47491455078125, 110.2366943359375, 58.028358459472656, 32.85905838012695, 32.69015884399414, 25.116430282592773, 23.677642822265625, 15.55703067779541, 14.857385635375977, 14.453474998474121, 12.77745532989502, 11.53365421295166, 11.316082000732422, 11.184483528137207, 10.910415649414062, 10.450433731079102, 10.132036209106445, 9.116124153137207, 8.4962739944458, 16.84276008605957, 7.747166633605957, 7.703784942626953, 7.233788013458252, 7.124403476715088, 7.018192291259766, 7.017589569091797, 6.894871234893799, 6.79086446762085, 6.5468363761901855, 6.500889301300049, 91.45791625976562, 19.532150268554688, 26.007572174072266, 10.7755126953125, 13.654305458068848, 220.0768585205078, 44.11783981323242, 38.49692153930664, 49.43640899658203, 35.770198822021484, 243.610107421875, 37.219730377197266, 163.27157592773438, 130.13299560546875, 113.45681762695312, 89.34529113769531, 20.24532699584961, 248.1008758544922, 201.02052307128906, 34.89019012451172, 71.83399963378906, 106.23780059814453, 92.84342956542969, 59.145328521728516, 33.75550079345703, 15.477336883544922, 15.39580249786377, 14.368524551391602, 13.790632247924805, 13.3402099609375, 12.825844764709473, 11.461099624633789, 11.314004898071289, 11.220707893371582, 10.86217212677002, 10.433804512023926, 9.818781852722168, 9.561084747314453, 9.340564727783203, 90.49833679199219, 8.824519157409668, 8.80889892578125, 18.42535400390625, 8.200715065002441, 8.037009239196777, 8.007038116455078, 7.960154056549072, 7.525711536407471, 7.1209187507629395, 7.1205549240112305, 6.945319652557373, 11.718214988708496, 264.621826171875, 248.1008758544922, 28.074190139770508, 723.941650390625, 13.318745613098145, 191.84494018554688, 291.72406005859375, 579.6676025390625, 27.766996383666992, 21.562824249267578, 130.13299560546875, 202.94290161132812, 46.05876922607422, 20.5611572265625, 482.9601135253906, 113.45681762695312], \"loglift\": [30.0, 29.0, 28.0, 27.0, 26.0, 25.0, 24.0, 23.0, 22.0, 21.0, 20.0, 19.0, 18.0, 17.0, 16.0, 15.0, 14.0, 13.0, 12.0, 11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 2.9217000007629395, 2.795300006866455, 2.79229998588562, 2.7890000343322754, 2.7799999713897705, 2.7718000411987305, 2.767899990081787, 2.763700008392334, 2.763200044631958, 2.750699996948242, 2.7506000995635986, 2.735599994659424, 2.733299970626831, 2.732599973678589, 2.727299928665161, 2.7149999141693115, 2.7121999263763428, 2.7049999237060547, 2.6970999240875244, 2.6893999576568604, 2.6740000247955322, 2.6579999923706055, 2.654599905014038, 2.6512999534606934, 2.6512999534606934, 2.6512999534606934, 2.65120005607605, 2.65120005607605, 2.648200035095215, 2.6465001106262207, 2.640199899673462, 2.6266000270843506, 2.5850000381469727, 2.4790000915527344, 2.611799955368042, 2.508500099182129, 2.271199941635132, 2.309799909591675, 2.234299898147583, 2.456700086593628, 2.4442999362945557, 1.8339999914169312, 1.5871000289916992, 1.7431999444961548, 1.511299967765808, 0.17440000176429749, 1.2259999513626099, 1.2660000324249268, 0.29100000858306885, -0.36230000853538513, 2.4163999557495117, 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\"M310\", \"M310\", \"M311~B\", \"M311~G\", \"M312\", \"M314\", \"M314\", \"M316~A\", \"M316~E2\", \"M317\", \"M317\", \"M317\", \"M317~A\", \"M317~A\", \"M318~A1\", \"M318~A1\", \"M319~C\", \"M320\", \"M320\", \"M320\", \"M321\", \"M321~B\", \"M321~C\", \"M321~HB\", \"M323\", \"M323\", \"M323~I\", \"M324~D\", \"M325\", \"M325\", \"M325\", \"M325\", \"M325~D\", \"M327\", \"M327\", \"M327\", \"M327\", \"M327\", \"M327~P\", \"M332\", \"M338\", \"M338\", \"M338~B\", \"M340\", \"M340\", \"M340_M054\", \"M341\", \"M341\", \"M341~Q\", \"M346\", \"M346~1\", \"M346~A\", \"M346~B\", \"M346~D\", \"M347\", \"M347\", \"M347\", \"M347_M377~E\", \"M348\", \"M348\", \"M348\", \"M348\", \"M352~C\", \"M352~N\", \"M352~O\", \"M352~O\", \"M352~O\", \"M354\", \"M359\", \"M362\", \"M362\", \"M362+X\", \"M362_M367\", \"M362_M367\", \"M362_M367_M269~A_M269~2\", \"M362~A\", \"M362~A\", \"M365\", \"M365\", \"M365\", \"M365\", \"M367\", \"M367\", \"M367\", \"M367~A\", \"M367~A\", \"M367~A_M346~A\", \"M367~C\", 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\"|M036+1(N30D)|\", \"|M036+1(N30D)|\", \"|M039~C+1(N30D)|\", \"|M054+M365+M054~I|\", \"|M106~2+M288|\", \"|M131+M388|\", \"|M131+M388|\", \"|M136+M365|\", \"|M136+M365|\", \"|M136+M365|\", \"|M136+X|\", \"|M136+X|\", \"|M153+M342|\", \"|M153+M377~E+M153|\", \"|M157+M288|\", \"|M157+X|\", \"|M157~A+M131~D|\", \"|M175+M136|\", \"|M175+M153|\", \"|M175+M175|\", \"|M175+M288|\", \"|M195+M038~A|\", \"|M195+M057|\", \"|M195+M057|\", \"|M195+X|\", \"|M201+M377+M377|\", \"|M217+M388|\", \"|M218+M288|\", \"|M218+M288|\", \"|M228+M101|\", \"|M260+1(N24)|\", \"|M260+1(N24)|\", \"|M260~1+1(N24)|\", \"|M260~2+1(N24)|\", \"|M260~2+1(N24)|\", \"|M278+1(N24)|\", \"|M288+1(N01)|\", \"|M305+M136|\", \"|M305+M320|\", \"|M305+M342|\", \"|M305+M342|\", \"|M305+M342|\", \"|M305+M342|\", \"|M305+X|\", \"|M305+X|\", \"|M305+X|\", \"|M305+X|\", \"|M317+X|\", \"|M327+M059|\", \"|M327+M342|\", \"|M327+M342|\", \"|M327+M342|\", \"|M327+M342|\", \"|M327+M342|\", \"|M327+X|\", \"|M327+X|\", \"|M351+1(N14)|\", \"|M351+1(N14)|\", \"|M351+1(N14)|\", \"|M351+1(N14)|\", \"|M351+X|\", \"|M362+X|\", \"|M370+M046+M370|\", \"|M370+M072+M370|\", \"|M370+M388+M370|\", \"|M370~B+M072|\", \"|M370~B+M388|\", \"|M377+M320+M377|\", \"|M377+M320+M377|\", \"|M377+M377|\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]};\n",
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      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "0     -0.069240  0.038265       1        1   5.302455\n",
       "1     -0.023029  0.201220       2        1   8.537845\n",
       "2     -0.048604 -0.175929       3        1   4.213594\n",
       "3     -0.174361  0.074868       4        1   7.071535\n",
       "4      0.158433 -0.007178       5        1  10.365279\n",
       "5      0.279921 -0.056461       6        1  27.525335\n",
       "6     -0.210595  0.018669       7        1   5.419681\n",
       "7     -0.076805 -0.001214       8        1   4.288581\n",
       "8     -0.014328 -0.254184       9        1  13.057682\n",
       "9      0.178608  0.161943      10        1  14.218016, topic_info=     Category        Freq              Term       Total  loglift  logprob\n",
       "23    Default  723.000000              M288  723.000000  30.0000  30.0000\n",
       "31    Default  222.000000              M346  222.000000  29.0000  29.0000\n",
       "106   Default  191.000000              M376  191.000000  28.0000  28.0000\n",
       "6     Default  579.000000              M388  579.000000  27.0000  27.0000\n",
       "10    Default  482.000000              M218  482.000000  26.0000  26.0000\n",
       "51    Default  264.000000              M124  264.000000  25.0000  25.0000\n",
       "32    Default  248.000000              M054  248.000000  24.0000  24.0000\n",
       "37    Default  243.000000              M297  243.000000  23.0000  23.0000\n",
       "62    Default  202.000000              M009  202.000000  22.0000  22.0000\n",
       "39    Default  202.000000              M096  202.000000  21.0000  21.0000\n",
       "24    Default  291.000000              M371  291.000000  20.0000  20.0000\n",
       "17    Default  220.000000              M387  220.000000  19.0000  19.0000\n",
       "3     Default  232.000000              M066  232.000000  18.0000  18.0000\n",
       "294   Default  111.000000              M036  111.000000  17.0000  17.0000\n",
       "124   Default  110.000000              M354  110.000000  16.0000  16.0000\n",
       "67    Default  100.000000              M032  100.000000  15.0000  15.0000\n",
       "16    Default  106.000000              M370  106.000000  14.0000  14.0000\n",
       "4     Default  201.000000              M157  201.000000  13.0000  13.0000\n",
       "48    Default   71.000000            M297~B   71.000000  12.0000  12.0000\n",
       "91    Default   92.000000              M072   92.000000  11.0000  11.0000\n",
       "430   Default   86.000000              M367   86.000000  10.0000  10.0000\n",
       "490   Default   91.000000    |M036+1(N30D)|   91.000000   9.0000   9.0000\n",
       "193   Default   54.000000            M056~F   54.000000   8.0000   8.0000\n",
       "235   Default   46.000000  M157_|M195+M057|   46.000000   7.0000   7.0000\n",
       "84    Default   90.000000            M003~B   90.000000   6.0000   6.0000\n",
       "799   Default   43.000000         M106_M009   43.000000   5.0000   5.0000\n",
       "12    Default  163.000000              M263  163.000000   4.0000   4.0000\n",
       "122   Default  113.000000              M305  113.000000   3.0000   3.0000\n",
       "69    Default   58.000000            M263~A   58.000000   2.0000   2.0000\n",
       "287   Default   59.000000              M373   59.000000   1.0000   1.0000\n",
       "...       ...         ...               ...         ...      ...      ...\n",
       "1011  Topic10    8.514569            M373~A    9.340565   1.8581  -5.5520\n",
       "84    Topic10   82.309402            M003~B   90.498337   1.8558  -3.2832\n",
       "953   Topic10    7.998386            M246~B    8.824519   1.8524  -5.6145\n",
       "923   Topic10    7.982806              M312    8.808899   1.8522  -5.6164\n",
       "322   Topic10   16.676380           M318~A1   18.425354   1.8509  -4.8797\n",
       "516   Topic10    7.374688     M387~I_M387~I    8.200715   1.8445  -5.6957\n",
       "975   Topic10    7.210835            M158~H    8.037009   1.8422  -5.7181\n",
       "420   Topic10    7.181046  |M370+M046+M370|    8.007038   1.8418  -5.7223\n",
       "933   Topic10    7.134115            M288~D    7.960154   1.8411  -5.7288\n",
       "755   Topic10    6.699657            M010~1    7.525712   1.8344  -5.7917\n",
       "272   Topic10    6.294528              M016    7.120919   1.8273  -5.8541\n",
       "164   Topic10    6.294036            M352~C    7.120555   1.8273  -5.8541\n",
       "620   Topic10    6.119283            M010~6    6.945320   1.8240  -5.8823\n",
       "103   Topic10   10.151665              M310   11.718215   1.8072  -5.3761\n",
       "51    Topic10  200.614471              M124  264.621826   1.6737  -2.3923\n",
       "32    Topic10  173.115738              M054  248.100876   1.5908  -2.5398\n",
       "22    Topic10   19.154432            M203~A   28.074190   1.5683  -4.7412\n",
       "23    Topic10  172.842758              M288  723.941650   0.5183  -2.5413\n",
       "431   Topic10    9.814070              M369   13.318746   1.6453  -5.4099\n",
       "106   Topic10   43.622520              M376  191.844940   0.4695  -3.9182\n",
       "24    Topic10   51.423927              M371  291.724060   0.2150  -3.7536\n",
       "6     Topic10   72.263634              M388  579.667603  -0.1315  -3.4134\n",
       "59    Topic10   13.970788     |M351+1(N14)|   27.766996   1.2638  -5.0568\n",
       "220   Topic10   11.689442          |M305+X|   21.562824   1.3384  -5.2350\n",
       "61    Topic10   17.040993              M001  130.132996  -0.0823  -4.8581\n",
       "62    Topic10   18.698141              M009  202.942902  -0.4338  -4.7653\n",
       "145   Topic10   12.780156            M005~A   46.058769   0.6686  -5.1458\n",
       "246   Topic10   10.580775            M380~B   20.561157   1.2863  -5.3347\n",
       "10    Topic10   14.578893              M218  482.960114  -1.5497  -5.0142\n",
       "122   Topic10   12.307783              M305  113.456818  -0.2705  -5.1835\n",
       "\n",
       "[498 rows x 6 columns], token_table=      Topic      Freq              Term\n",
       "term                                   \n",
       "61        1  0.038422              M001\n",
       "61        2  0.030738              M001\n",
       "61        4  0.253587              M001\n",
       "61        5  0.099898              M001\n",
       "61        6  0.169058              M001\n",
       "61        9  0.276640              M001\n",
       "61       10  0.130636              M001\n",
       "470       5  0.889228            M001~B\n",
       "495       9  0.978888              M002\n",
       "594       3  0.758453            M002~B\n",
       "594       9  0.216701            M002~B\n",
       "909       2  0.936052              M003\n",
       "84        3  0.055250            M003~B\n",
       "84        8  0.033150            M003~B\n",
       "84       10  0.906094            M003~B\n",
       "135       1  0.027034              M004\n",
       "135       6  0.937191              M004\n",
       "135      10  0.027034              M004\n",
       "85        1  0.656634              M005\n",
       "85        4  0.262654              M005\n",
       "85        7  0.065663              M005\n",
       "145       2  0.217114            M005~A\n",
       "145       4  0.108557            M005~A\n",
       "145       5  0.390805            M005~A\n",
       "145      10  0.282248            M005~A\n",
       "58        2  0.219850              M006\n",
       "58        4  0.610694              M006\n",
       "58        9  0.170994              M006\n",
       "834       4  0.924214            M006@G\n",
       "835       4  0.933262       M006_M362~A\n",
       "...     ...       ...               ...\n",
       "219       5  0.270443       |M305+M342|\n",
       "219       6  0.225369       |M305+M342|\n",
       "219       8  0.135221       |M305+M342|\n",
       "220       2  0.185504          |M305+X|\n",
       "220       5  0.139128          |M305+X|\n",
       "220       8  0.092752          |M305+X|\n",
       "220      10  0.556513          |M305+X|\n",
       "583       3  0.784978          |M317+X|\n",
       "365       5  0.953265       |M327+M059|\n",
       "232       2  0.353845       |M327+M342|\n",
       "232       4  0.096503       |M327+M342|\n",
       "232       6  0.257342       |M327+M342|\n",
       "232       9  0.193006       |M327+M342|\n",
       "232      10  0.096503       |M327+M342|\n",
       "341       2  0.152359          |M327+X|\n",
       "341       5  0.812583          |M327+X|\n",
       "59        1  0.252098     |M351+1(N14)|\n",
       "59        5  0.180070     |M351+1(N14)|\n",
       "59        8  0.036014     |M351+1(N14)|\n",
       "59       10  0.504196     |M351+1(N14)|\n",
       "1421      9  0.941589          |M351+X|\n",
       "820       7  0.889419          |M362+X|\n",
       "420      10  0.874231  |M370+M046+M370|\n",
       "288      10  0.959768  |M370+M072+M370|\n",
       "290      10  0.958423  |M370+M388+M370|\n",
       "291      10  0.974292     |M370~B+M072|\n",
       "292      10  0.974352     |M370~B+M388|\n",
       "655       2  0.600995  |M377+M320+M377|\n",
       "655       5  0.300497  |M377+M320+M377|\n",
       "713       8  0.898777       |M377+M377|\n",
       "\n",
       "[647 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pyLDAvis\n",
    "import pyLDAvis.gensim\n",
    "pyLDAvis.enable_notebook()\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter('ignore')\n",
    "    vis = pyLDAvis.gensim.prepare(lda_model, corpus, dictionary, sort_topics=False)\n",
    "    display(vis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2. Representative Texts\n",
    "\n",
    "Another way to investigate the topic model is to print the most representative signs and tablets for each topic. For ease of interpretation, the following code block color-codes signs according to the topic they most strongly predict. (Colors may not display depending on where you view the notebook - run a local copy for best results.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Formatting topic data...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><body><h1>Topic 1:</h1><hr/><p><h2>Representative terms:</h2><br/><span style='margin-left:2em;'>M376 (18.02%)</span><br/><span style='margin-left:2em;'>M056~F (6.60%)</span><br/><span style='margin-left:2em;'>M032 (6.32%)</span><br/><span style='margin-left:2em;'>|M136+M365| (3.38%)</span><br/><span style='margin-left:2em;'>M365 (3.15%)</span><br/><span style='margin-left:2em;'>M149~A (2.30%)</span><br/><span style='margin-left:2em;'>M374~C (2.01%)</span><br/><span style='margin-left:2em;'>M203~C (2.01%)</span><br/><span style='margin-left:2em;'>M157 (1.55%)</span><br/><span style='margin-left:2em;'>M005 (1.21%)</span></p><p><h2>Representative tablets:</h2><br/></p><h3>P008317</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#1CE6FF;'>Topic 1: 97.27%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, </span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">X </span><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#1ce6ff;\">M376 </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#1ce6ff;\">M206~B </span><span style=\"color:#1ce6ff;\">M004 </span><span style=\"color:#1ce6ff;\">M376 </span><span style='color:#bbb;'>, 1(N08A)#?</span><br /><span style=\"color:#1ce6ff;\">M106~B </span><span style=\"color:#1ce6ff;\">M317 </span><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#1ce6ff;\">M376 </span><span style='color:#bbb;'>, 6(N01)</span><br /><span style=\"color:#1ce6ff;\">M203~C </span><span style=\"color:#1ce6ff;\">|M136+M365| </span><span style=\"color:#1ce6ff;\">M203~A </span><span style='color:#bbb;'>, 2(N14)</span><br /><span style=\"color:#1ce6ff;\">M376~B </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#1ce6ff;\">M032 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#1ce6ff;\">M104 </span><span style=\"color:#000;\">X </span><span style=\"color:#1ce6ff;\">|M228+M320| </span><span style=\"color:#1ce6ff;\">M032 </span><span style='color:#bbb;'>, 4(N01)</span><br /><span style=\"color:#1ce6ff;\">M321~B </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#1ce6ff;\">|M305+M009| </span><span style=\"color:#1ce6ff;\">M175 </span><span style=\"color:#1ce6ff;\">M321~D </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#1ce6ff;\">M321~HB </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#1ce6ff;\">M048~I </span><span style=\"color:#1ce6ff;\">M175 </span><span style=\"color:#1ce6ff;\">|M305+M009| </span><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#1ce6ff;\">M321~B </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#1ce6ff;\">M005 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#1ce6ff;\">M376 </span><span style='color:#bbb;'>, 1(N14)</span><br /><span style=\"color:#1ce6ff;\">M376~B </span><span style='color:#bbb;'>, 1(N14)</span><br /><span style=\"color:#1ce6ff;\">M203~A </span><span style='color:#bbb;'>, 1(N14)</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 2(N01)#</span><br /><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#1ce6ff;\">M376 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#000;\">X </span><span style=\"color:#1ce6ff;\">M376~B </span><span style='color:#bbb;'>, 1(N08A)#?</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, </span><br /><span style=\"color:#1ce6ff;\">M321~B </span><span style='color:#bbb;'>, 8(N01)#</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 2(N14)#</span><br /><span style=\"color:#1ce6ff;\">M376~B </span><span style='color:#bbb;'>, 4(N14) 5(N01) 1(N08A)</span><br /></div><h3>P008305</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#1CE6FF;'>Topic 1: 96.90%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, </span><br /><span style=\"color:#000;\">[...] </span><span style=\"color:#1ce6ff;\">M157 </span><span style=\"color:#1ce6ff;\">|M305+M342| </span><span style=\"color:#1ce6ff;\">M228~GA </span><span style='color:#bbb;'>, 2(N14)</span><br /><span style=\"color:#1ce6ff;\">M264~A </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 1(N14)</span><br /><span style=\"color:#1ce6ff;\">|M260+1(N24)| </span><span style='color:#bbb;'>, 3(N14)</span><br /><span style=\"color:#1ce6ff;\">|M136+M365| </span><span style=\"color:#1ce6ff;\">M228~GA </span><span style='color:#bbb;'>, 2(N14)</span><br /><span style=\"color:#1ce6ff;\">M264~A </span><span style='color:#bbb;'>, 4(N14)</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 2(N14)#</span><br /><span style=\"color:#1ce6ff;\">M111~A </span><span style=\"color:#1ce6ff;\">M228~GA </span><span style='color:#bbb;'>, 2(N14)</span><br /><span style=\"color:#1ce6ff;\">M264~A </span><span style='color:#bbb;'>, 4(N14)</span><br /><span style=\"color:#1ce6ff;\">|M260+1(N24)| </span><span style='color:#bbb;'>, 2(N14)</span><br /><span style=\"color:#000;\">X </span><span style=\"color:#1ce6ff;\">M228~GA </span><span style=\"color:#1ce6ff;\">M264~A </span><span style='color:#bbb;'>, 1(N34) 3(N14)</span><br /><span style=\"color:#1ce6ff;\">|M136+M365| </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#1ce6ff;\">M228~GA </span><span style=\"color:#1ce6ff;\">M264~A </span><span style=\"color:#1ce6ff;\">M261~A1 </span><span style='color:#bbb;'>, 1(N34) 2(N14)</span><br /><span style=\"color:#1ce6ff;\">M111~A </span><span style=\"color:#1ce6ff;\">M228~GA </span><span style=\"color:#1ce6ff;\">M264~A </span><span style='color:#bbb;'>, 1(N34) [...]</span><br /><span style=\"color:#1ce6ff;\">|M351+1(N14)| </span><span style='color:#bbb;'>, 4(N01)# [...]</span><br /></div><h3>P009365</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#1CE6FF;'>Topic 1: 94.00%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#000;\">[... </span><span style=\"color:#1ce6ff;\">M278~F] </span><span style='color:#bbb;'>, [3(N14)] 8(N01)#</span><br /><span style=\"color:#1ce6ff;\">M278~F </span><span style='color:#bbb;'>, 2(N14) [5(N01)]</span><br /><span style=\"color:#1ce6ff;\">[|M278+1(N24)|] </span><span style='color:#bbb;'>, 3(N01)#</span><br /><span style=\"color:#1ce6ff;\">M283~A1 </span><span style='color:#bbb;'>, 5(N01)</span><br /><span style=\"color:#1ce6ff;\">M276~E </span><span style='color:#bbb;'>, 5(N01)</span><br /><span style=\"color:#1ce6ff;\">M290~A </span><span style='color:#bbb;'>, 7(N01)</span><br /><span style=\"color:#1ce6ff;\">M064~A </span><span style='color:#bbb;'>, 1(N14)</span><br /><span style=\"color:#1ce6ff;\">M228~GB </span><span style=\"color:#1ce6ff;\">M278~F </span><span style=\"color:#1ce6ff;\">|M351+1(N14)| </span><span style='color:#bbb;'>, 1(N34) 3(N01)</span><br /><span style=\"color:#1ce6ff;\">|M278+1(N24)| </span><span style='color:#bbb;'>, [3(N01)]</span><br /><span style=\"color:#1ce6ff;\">M290~A </span><span style='color:#bbb;'>, 7(N01)</span><br /><span style=\"color:#1ce6ff;\">M064~A </span><span style='color:#bbb;'>, 1(N14)</span><br /><span style=\"color:#1ce6ff;\">M276~E </span><span style='color:#bbb;'>, 5(N01)</span><br /></div><h1>Topic 2:</h1><hr/><p><h2>Representative terms:</h2><br/><span style='margin-left:2em;'>M288 (37.34%)</span><br/><span style='margin-left:2em;'>M157 (2.50%)</span><br/><span style='margin-left:2em;'>M391 (1.90%)</span><br/><span style='margin-left:2em;'>M304 (1.79%)</span><br/><span style='margin-left:2em;'>M054~I (1.75%)</span><br/><span style='margin-left:2em;'>M367 (1.73%)</span><br/><span style='margin-left:2em;'>M195 (1.40%)</span><br/><span style='margin-left:2em;'>M059 (1.13%)</span><br/><span style='margin-left:2em;'>M157~A (1.13%)</span><br/><span style='margin-left:2em;'>M325 (0.98%)</span></p><p><h2>Representative tablets:</h2><br/></p><h3>P008322</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#FF34FF;'>Topic 2: 98.47%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#ff34ff;\">|M327+M342| </span><span style='color:#bbb;'>, </span><br /><span style=\"color:#ff34ff;\">M054~B </span><span style='color:#bbb;'>, 4(N14)</span><br /><span style=\"color:#ff34ff;\">M328~B </span><span style='color:#bbb;'>, 2(N14)# 4(N01)# [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#ff34ff;\">M252~L </span><span style=\"color:#ff34ff;\">M054~I </span><span style=\"color:#ff34ff;\">M059~D </span><span style='color:#bbb;'>, 1(N14) 2(N01)</span><br /><span style=\"color:#ff34ff;\">M136~I </span><span style='color:#bbb;'>, 1(N14) 2(N01)</span><br /><span style=\"color:#ff34ff;\">M383~A </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style=\"color:#ff34ff;\">M106~B </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#ff34ff;\">M101 </span><span style='color:#bbb;'>, 1(N14) 4(N01)</span><br /><span style=\"color:#ff34ff;\">M418~B </span><span style='color:#bbb;'>, 7(N01)</span><br /><span style=\"color:#ff34ff;\">|M157~A+M367| </span><span style='color:#bbb;'>, 1(N14) 6(N01)</span><br /><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style=\"color:#ff34ff;\">M342 </span><span style=\"color:#ff34ff;\">M006 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#ff34ff;\">M158 </span><span style='color:#bbb;'>, 8(N01)</span><br /><span style=\"color:#ff34ff;\">M105~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#ff34ff;\">M387~H </span><span style=\"color:#ff34ff;\">M367 </span><span style='color:#bbb;'>, 9(N01)</span><br /><span style=\"color:#ff34ff;\">M054~I </span><span style=\"color:#ff34ff;\">M006 </span><span style='color:#bbb;'>, 6(N01)#</span><br /><span style=\"color:#ff34ff;\">M001 </span><span style=\"color:#ff34ff;\">M367 </span><span style='color:#bbb;'>, 6(N01)#</span><br /><span style=\"color:#ff34ff;\">M136~I </span><span style='color:#bbb;'>, 4(N01)</span><br /><span style=\"color:#ff34ff;\">M367 </span><span style=\"color:#ff34ff;\">M193~F </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#ff34ff;\">M134~A </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#ff34ff;\">M325~D </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#ff34ff;\">M059~B </span><span style='color:#bbb;'>, 7(N01)#?</span><br /><span style=\"color:#ff34ff;\">M105~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#ff34ff;\">M151~E </span><span style=\"color:#ff34ff;\">M059~B </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#ff34ff;\">M367 </span><span style=\"color:#ff34ff;\">M105~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#ff34ff;\">M348 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#ff34ff;\">M304 </span><span style=\"color:#ff34ff;\">M006 </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#ff34ff;\">M328~B </span><span style=\"color:#ff34ff;\">M059~D </span><span style='color:#bbb;'>, 2(N14) [...] 1(N01)# [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#ff34ff;\">M136 </span><span style='color:#bbb;'>, 2(N14) 2(N01)</span><br /><span style=\"color:#ff34ff;\">M003 </span><span style='color:#bbb;'>, 1(N14) 4(N01)#</span><br /><span style=\"color:#ff34ff;\">M193~F </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#ff34ff;\">M325 </span><span style=\"color:#ff34ff;\">M378 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#ff34ff;\">M210 </span><span style='color:#bbb;'>, 1(N14) 4(N01)</span><br /><span style=\"color:#ff34ff;\">M105~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#ff34ff;\">M003 </span><span style='color:#bbb;'>, 8(N01)#</span><br /><span style=\"color:#ff34ff;\">M054~I </span><span style='color:#bbb;'>, 1(N14)# 7(N01)#</span><br /><span style=\"color:#ff34ff;\">M105~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#ff34ff;\">M003 </span><span style='color:#bbb;'>, 6(N01)#</span><br /><span style=\"color:#ff34ff;\">M105~A </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#ff34ff;\">M365 </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#ff34ff;\">M003 </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#ff34ff;\">M383~K </span><span style='color:#bbb;'>, 8(N01)#</span><br /><span style=\"color:#ff34ff;\">M368~C </span><span style='color:#bbb;'>, 6(N01)</span><br /><span style=\"color:#ff34ff;\">M003 </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#ff34ff;\">M105~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#ff34ff;\">M074~B </span><span style=\"color:#ff34ff;\">M316~Q </span><span style='color:#bbb;'>, 1(N01)</span><br /></div><h3>P008035</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#FF34FF;'>Topic 2: 94.00%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#ff34ff;\">[M157] </span><span style='color:#bbb;'>, </span><br /><span style=\"color:#ff34ff;\">|M195+M038~A| </span><span style=\"color:#ff34ff;\">M249~N1 </span><span style=\"color:#ff34ff;\">M288 </span><span style='color:#bbb;'>, 4(N14)</span><br /><span style=\"color:#ff34ff;\">M134~E </span><span style='color:#bbb;'>, 3(N14)</span><br /><span style=\"color:#ff34ff;\">|M218+M218| </span><span style='color:#bbb;'>, 5(N01)</span><br /><span style=\"color:#ff34ff;\">M158 </span><span style='color:#bbb;'>, 1(N14) 3(N01)</span><br /><span style=\"color:#ff34ff;\">M180 </span><span style='color:#bbb;'>, 1(N14) 3(N01)</span><br /><span style=\"color:#ff34ff;\">M075~B </span><span style='color:#bbb;'>, 3(N14)# 2(N01)</span><br /><span style=\"color:#ff34ff;\">M348 </span><span style=\"color:#ff34ff;\">M288 </span><span style='color:#bbb;'>, [5(N14)] 5(N01)</span><br /><span style=\"color:#ff34ff;\">M288 </span><span style='color:#bbb;'>, 1(N45) 4(N14) 1(N01)</span><br /><span style=\"color:#ff34ff;\">M348 </span><span style=\"color:#ff34ff;\">M288 </span><span style='color:#bbb;'>, 5(N14) 5(N01)#</span><br /></div><h3>P008057</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#FF34FF;'>Topic 2: 91.82%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#000;\">[...] </span><span style=\"color:#ff34ff;\">|M106+M288| </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...] 2(N39B)</span><br /><span style=\"color:#ff34ff;\">M391 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#ff34ff;\">M288 </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...] 2(N01) 2(N39B)</span><br /><span style=\"color:#ff34ff;\">M391 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#ff34ff;\">M288 </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...] 1(N01)</span><br /><span style=\"color:#ff34ff;\">M288 </span><span style='color:#bbb;'>, 1(N34) 1(N45)# [...]</span><br /><span style=\"color:#ff34ff;\">M288 </span><span style='color:#bbb;'>, 2(N45)#</span><br /><span style=\"color:#ff34ff;\">M295~KA </span><span style=\"color:#ff34ff;\">M157~A </span><span style=\"color:#ff34ff;\">M288 </span><span style='color:#bbb;'>, 1(N34)</span><br /></div><h1>Topic 3:</h1><hr/><p><h2>Representative terms:</h2><br/><span style='margin-left:2em;'>M297~B (7.90%)</span><br/><span style='margin-left:2em;'>M297 (5.99%)</span><br/><span style='margin-left:2em;'>M157 (4.60%)</span><br/><span style='margin-left:2em;'>M341 (3.36%)</span><br/><span style='margin-left:2em;'>M139 (3.25%)</span><br/><span style='margin-left:2em;'>M387~L (3.15%)</span><br/><span style='margin-left:2em;'>M101 (2.52%)</span><br/><span style='margin-left:2em;'>|M036+1(N30D)| (2.18%)</span><br/><span style='margin-left:2em;'>M038~A (1.70%)</span><br/><span style='margin-left:2em;'>M286 (1.69%)</span></p><p><h2>Representative tablets:</h2><br/></p><h3>P008457</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#FF4A46;'>Topic 3: 92.50%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#ff4a46;\">M217~M </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#ff4a46;\">M387~A </span><span style=\"color:#ff4a46;\">|M036+1(N30D)| </span><span style='color:#bbb;'>, 1(N14)</span><br /><span style=\"color:#000;\">[...] </span><span style=\"color:#ff4a46;\">M361~C1 </span><span style=\"color:#ff4a46;\">M361~B1 </span><span style=\"color:#ff4a46;\">M080~CA </span><span style=\"color:#ff4a46;\">|M036+1(N30D)| </span><span style='color:#bbb;'>, 1(N14)#</span><br /><span style=\"color:#000;\">[...] </span><span style=\"color:#ff4a46;\">M341 </span><span style=\"color:#ff4a46;\">M038~A </span><span style=\"color:#ff4a46;\">M296 </span><span style='color:#bbb;'>, 1(N39B)</span><br /><span style=\"color:#ff4a46;\">M038~A </span><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /></div><h3>P008406</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#FF4A46;'>Topic 3: 85.00%</span><br/><span style='margin-left:2em;color:#63FFAC;'>Topic 9: 1.67%</span><br/><span style='margin-left:2em;color:#A30059;'>Topic 6: 1.67%</span><br/><span style='margin-left:2em;color:#006FA6;'>Topic 5: 1.67%</span><br/><span style='margin-left:2em;color:#0000A6;'>Topic 8: 1.67%</span><br/><span style='margin-left:2em;color:#B79762;'>Topic 10: 1.67%</span><br/><span style='margin-left:2em;color:#1CE6FF;'>Topic 1: 1.67%</span><br/><span style='margin-left:2em;color:#FF34FF;'>Topic 2: 1.67%</span><br/><span style='margin-left:2em;color:#008941;'>Topic 4: 1.67%</span><br/><span style='margin-left:2em;color:#7A4900;'>Topic 7: 1.67%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, </span><br /><span style=\"color:#ff4a46;\">|M175+X| </span><span style=\"color:#ff4a46;\">M387~EE </span><span style=\"color:#ff4a46;\">|M036+1(N30D)| </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...] 1(N01)#</span><br /><span style=\"color:#ff4a46;\">M263 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#ff4a46;\">M297 </span><span style='color:#bbb;'>, 2(N39B) 2(N30C) 1(N30D)</span><br /></div><h3>P008993</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#FF4A46;'>Topic 3: 82.00%</span><br/><span style='margin-left:2em;color:#0000A6;'>Topic 8: 2.00%</span><br/><span style='margin-left:2em;color:#006FA6;'>Topic 5: 2.00%</span><br/><span style='margin-left:2em;color:#FF34FF;'>Topic 2: 2.00%</span><br/><span style='margin-left:2em;color:#63FFAC;'>Topic 9: 2.00%</span><br/><span style='margin-left:2em;color:#1CE6FF;'>Topic 1: 2.00%</span><br/><span style='margin-left:2em;color:#A30059;'>Topic 6: 2.00%</span><br/><span style='margin-left:2em;color:#B79762;'>Topic 10: 2.00%</span><br/><span style='margin-left:2em;color:#008941;'>Topic 4: 2.00%</span><br/><span style='margin-left:2em;color:#7A4900;'>Topic 7: 2.00%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#ff4a46;\">M157 </span><span style='color:#bbb;'>, </span><br /><span style=\"color:#ff4a46;\">|M305+M136| </span><span style=\"color:#ff4a46;\">M264~D1 </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#ff4a46;\">M297~B </span><span style='color:#bbb;'>, 2(N01)#</span><br /><span style=\"color:#000;\">X </span><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, 2(N39B) 1(N24)</span><br /></div><h1>Topic 4:</h1><hr/><p><h2>Representative terms:</h2><br/><span style='margin-left:2em;'>M346 (20.29%)</span><br/><span style='margin-left:2em;'>M367 (5.38%)</span><br/><span style='margin-left:2em;'>M009 (3.97%)</span><br/><span style='margin-left:2em;'>M001 (3.04%)</span><br/><span style='margin-left:2em;'>M059 (2.95%)</span><br/><span style='margin-left:2em;'>M006 (2.28%)</span><br/><span style='margin-left:2em;'>M153 (2.09%)</span><br/><span style='margin-left:2em;'>M292~F_M102~D (2.03%)</span><br/><span style='margin-left:2em;'>M292~F (1.62%)</span><br/><span style='margin-left:2em;'>|M131+M388| (1.51%)</span></p><p><h2>Representative tablets:</h2><br/></p><h3>P008294</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#008941;'>Topic 4: 98.97%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#008941;\">|M362+M005| </span><span style=\"color:#008941;\">M207~B </span><span style='color:#bbb;'>, 3(N01)#</span><br /><span style=\"color:#008941;\">M367 </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#008941;\">M346 </span><span style='color:#bbb;'>, 4(N01)</span><br /><span style=\"color:#008941;\">M362~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#008941;\">M346~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#008941;\">|M362+M059~D| </span><span style=\"color:#008941;\">M312~A </span><span style='color:#bbb;'>, 6(N01)#</span><br /><span style=\"color:#008941;\">M367 </span><span style='color:#bbb;'>, 4(N01)#</span><br /><span style=\"color:#008941;\">M346 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#008941;\">M362~A </span><span style='color:#bbb;'>, 2(N14) 3(N01)</span><br /><span style=\"color:#008941;\">M367~A </span><span style='color:#bbb;'>, 7(N01)</span><br /><span style=\"color:#008941;\">M346~A </span><span style='color:#bbb;'>, 1(N14)</span><br /><span style=\"color:#008941;\">M006@G </span><span style='color:#bbb;'>, 3(N01)#</span><br /><span style=\"color:#008941;\">|M362+M384~A| </span><span style='color:#bbb;'>, 4(N14) 7(N01)</span><br /><span style=\"color:#008941;\">M367 </span><span style='color:#bbb;'>, 8(N01)</span><br /><span style=\"color:#008941;\">M346 </span><span style='color:#bbb;'>, 6(N01)</span><br /><span style=\"color:#008941;\">M006 </span><span style='color:#bbb;'>, 6(N01)</span><br /><span style=\"color:#008941;\">M362~A </span><span style='color:#bbb;'>, 1(N14)</span><br /><span style=\"color:#008941;\">M367~A </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#008941;\">M346~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#008941;\">M006@G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#008941;\">|M362+M059| </span><span style=\"color:#008941;\">|M001+M379~C| </span><span style='color:#bbb;'>, 4(N14)</span><br /><span style=\"color:#008941;\">M367 </span><span style='color:#bbb;'>, 9(N01)#</span><br /><span style=\"color:#008941;\">[M346] </span><span style='color:#bbb;'>, 2(N01)#</span><br /><span style=\"color:#008941;\">M006 </span><span style='color:#bbb;'>, 3(N01)#</span><br /><span style=\"color:#008941;\">M362~A </span><span style='color:#bbb;'>, 6(N01)</span><br /><span style=\"color:#008941;\">M367~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#008941;\">M346~A </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#008941;\">M006@G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#008941;\">|M362+M383~C| </span><span style='color:#bbb;'>, 1(N14)</span><br /><span style=\"color:#008941;\">[M367] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#008941;\">M346 </span><span style='color:#bbb;'>, 6(N01)#</span><br /><span style=\"color:#008941;\">M006 </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#008941;\">M362~A </span><span style='color:#bbb;'>, 2(N14)# 1(N01)#</span><br /><span style=\"color:#008941;\">[M367~A] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#008941;\">[M346~A] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#008941;\">[M006@G] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#008941;\">M006@G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#008941;\">|M362+M158| </span><span style='color:#bbb;'>, 1(N14) [...]</span><br /><span style=\"color:#008941;\">M367 </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#008941;\">M362~A </span><span style='color:#bbb;'>, 4(N01)</span><br /><span style=\"color:#008941;\">|M362+M026~H| </span><span style='color:#bbb;'>, 5(N01)</span><br /><span style=\"color:#008941;\">M367 </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#008941;\">M346 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#008941;\">M362~A </span><span style='color:#bbb;'>, 6(N01)#</span><br /><span style=\"color:#008941;\">[M367~A] </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#008941;\">M346~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#008941;\">M006@G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#008941;\">M351~C </span><span style=\"color:#008941;\">M362 </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#008941;\">M367 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#008941;\">M362~A </span><span style='color:#bbb;'>, 1(N14) 1(N01)</span><br /><span style=\"color:#008941;\">M367~A </span><span style='color:#bbb;'>, 2(N01)#</span><br /><span style=\"color:#008941;\">[M346~A] </span><span style='color:#bbb;'>, 2(N01)#</span><br /><span style=\"color:#008941;\">M006@G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#008941;\">|M362+M244| </span><span style='color:#bbb;'>, 3(N14) 3(N01)</span><br /><span style=\"color:#008941;\">M367 </span><span style='color:#bbb;'>, 6(N01)</span><br /><span style=\"color:#008941;\">M346 </span><span style='color:#bbb;'>, 1(N14) 4(N01)#</span><br /><span style=\"color:#008941;\">[M006] </span><span style='color:#bbb;'>, 2(N01)#?</span><br /><span style=\"color:#008941;\">|M362+M099~B| </span><span style='color:#bbb;'>, 2(N14) 1(N01)</span><br /><span style=\"color:#008941;\">M367 </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#008941;\">M346 </span><span style='color:#bbb;'>, 1(N14) 9(N01)</span><br /><span style=\"color:#008941;\">[M006] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#008941;\">M362~A </span><span style='color:#bbb;'>, 6(N14)#</span><br /><span style=\"color:#008941;\">[M367~A] </span><span style='color:#bbb;'>, 4(N01)#</span><br /><span style=\"color:#008941;\">M346~A </span><span style='color:#bbb;'>, 1(N14) 6(N01)</span><br /><span style=\"color:#008941;\">M006@G </span><span style='color:#bbb;'>, 7(N01)</span><br /></div><h3>P008349</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#008941;'>Topic 4: 90.55%</span><br/><span style='margin-left:2em;color:#7A4900;'>Topic 7: 7.09%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#008941;\">|M136+M365| </span><span style='color:#bbb;'>, </span><br /><span style=\"color:#008941;\">M125 </span><span style=\"color:#000;\">X </span><span style=\"color:#008941;\">M359 </span><span style=\"color:#008941;\">M367 </span><span style='color:#bbb;'>, 4(N01)</span><br /><span style=\"color:#008941;\">M269~C </span><span style='color:#bbb;'>, 1(N08A)</span><br /><span style=\"color:#7a4900;\">M106~A </span><span style='color:#bbb;'>, 2(N30C)</span><br /><span style=\"color:#008941;\">M009 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#008941;\">M292~F </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#008941;\">M102~D </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#008941;\">M246~M </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#008941;\">M209~C </span><span style=\"color:#008941;\">M388 </span><span style=\"color:#008941;\">M080~A </span><span style=\"color:#000;\">X </span><span style=\"color:#008941;\">M367 </span><span style='color:#bbb;'>, 4(N01)</span><br /><span style=\"color:#008941;\">M269~C </span><span style='color:#bbb;'>, 1(N08)#?</span><br /><span style=\"color:#7a4900;\">M106~A </span><span style='color:#bbb;'>, 2(N30C)</span><br /><span style=\"color:#008941;\">M009 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#008941;\">M292~F </span><span style='color:#bbb;'>, [1(N01)]</span><br /><span style=\"color:#008941;\">[M102~D] </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#008941;\">M246~M </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#008941;\">M009 </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#008941;\">M292~F </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#008941;\">M102~D </span><span style='color:#bbb;'>, 4(N01)</span><br /><span style=\"color:#008941;\">M246~M </span><span style='color:#bbb;'>, 2(N01)</span><br /></div><h3>P008901</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#008941;'>Topic 4: 89.91%</span><br/><span style='margin-left:2em;color:#B79762;'>Topic 10: 6.09%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#008941;\">M001 </span><span style='color:#bbb;'>, 2(N01)#</span><br /><span style=\"color:#008941;\">M001 </span><span style='color:#bbb;'>, 4(N01)</span><br /><span style=\"color:#008941;\">M001 </span><span style='color:#bbb;'>, 5(N01)</span><br /><span style=\"color:#008941;\">M001 </span><span style='color:#bbb;'>, 2(N01)#</span><br /><span style=\"color:#008941;\">M009~G </span><span style='color:#bbb;'>, 1(N14) 4(N01)</span><br /><span style=\"color:#008941;\">M009~G </span><span style='color:#bbb;'>, 1(N14)</span><br /><span style=\"color:#008941;\">M012 </span><span style='color:#bbb;'>, 6(N01)</span><br /><span style=\"color:#008941;\">M009~H </span><span style='color:#bbb;'>, 5(N01)#</span><br /><span style=\"color:#008941;\">M001 </span><span style=\"color:#008941;\">M009~I </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#008941;\">M026~D </span><span style='color:#bbb;'>, 1(N14) 7(N01)</span><br /><span style=\"color:#008941;\">M032~B </span><span style='color:#bbb;'>, 1(N14) 6(N01)</span><br /><span style=\"color:#008941;\">M001 </span><span style='color:#bbb;'>, 1(N14) 6(N01)</span><br /><span style=\"color:#008941;\">M001 </span><span style='color:#bbb;'>, 2(N14)</span><br /><span style=\"color:#008941;\">M001 </span><span style='color:#bbb;'>, 2(N14) 1(N01)</span><br /><span style=\"color:#008941;\">M001 </span><span style='color:#bbb;'>, 5(N01)#</span><br /><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, 1(N14)# 1(N01)</span><br /><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, 3(N01)#</span><br /><span style=\"color:#008941;\">M001 </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#008941;\">M009 </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">M016 </span><span style='color:#bbb;'>, 7(N01)</span><br /></div><h1>Topic 5:</h1><hr/><p><h2>Representative terms:</h2><br/><span style='margin-left:2em;'>M388 (10.39%)</span><br/><span style='margin-left:2em;'>M297 (3.72%)</span><br/><span style='margin-left:2em;'>M305 (3.67%)</span><br/><span style='margin-left:2em;'>M157 (3.30%)</span><br/><span style='margin-left:2em;'>M054 (2.75%)</span><br/><span style='margin-left:2em;'>M387~C (2.52%)</span><br/><span style='margin-left:2em;'>M317 (2.22%)</span><br/><span style='margin-left:2em;'>M288 (2.20%)</span><br/><span style='margin-left:2em;'>M157~A (1.69%)</span><br/><span style='margin-left:2em;'>M206~D (1.69%)</span></p><p><h2>Representative tablets:</h2><br/></p><h3>P008303</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#006FA6;'>Topic 5: 96.40%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 7(N01)#</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 4(N01)</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#000;\">X </span><span style=\"color:#006fa6;\">M418~B </span><span style='color:#bbb;'>, 1(N14) 9(N01)</span><br /><span style=\"color:#006fa6;\">M418~C </span><span style='color:#bbb;'>, 6(N01)</span><br /><span style=\"color:#006fa6;\">M418~A </span><span style='color:#bbb;'>, 1(N14)</span><br /><span style=\"color:#006fa6;\">M418~C </span><span style='color:#bbb;'>, 6(N01)</span><br /><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#006fa6;\">M418 </span><span style='color:#bbb;'>, 1(N14) 3(N01)</span><br /><span style=\"color:#006fa6;\">M418~C </span><span style='color:#bbb;'>, 6(N01)</span><br /><span style=\"color:#006fa6;\">M418~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#006fa6;\">M418~C </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#006fa6;\">M059 </span><span style=\"color:#006fa6;\">M418~B </span><span style='color:#bbb;'>, 8(N01)</span><br /><span style=\"color:#006fa6;\">M418~C </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#006fa6;\">M418~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#006fa6;\">M418~C </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#006fa6;\">M207~B </span><span style=\"color:#006fa6;\">M418~B </span><span style='color:#bbb;'>, 1(N14) 8(N01)</span><br /><span style=\"color:#006fa6;\">M418~C </span><span style='color:#bbb;'>, 4(N01)</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 3(N01)#</span><br /><span style=\"color:#006fa6;\">M418~C </span><span style='color:#bbb;'>, 4(N01)#</span><br /><span style=\"color:#006fa6;\">M210~E </span><span style=\"color:#006fa6;\">M418~B </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#006fa6;\">M418~C </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#006fa6;\">M383~C </span><span style=\"color:#006fa6;\">M418~B </span><span style='color:#bbb;'>, 8(N01)</span><br /><span style=\"color:#006fa6;\">M418 </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#006fa6;\">M418~A </span><span style='color:#bbb;'>, 2(N14) 6(N01)</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 2(N14) 5(N01)</span><br /><span style='color:#bbb;'>, 1(N23) 8(N14) 5(N01)</span><br /></div><h3>P009237</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#006FA6;'>Topic 5: 91.82%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#006fa6;\">M157~A </span><span style='color:#bbb;'>, </span><br /><span style=\"color:#006fa6;\">M340 </span><span style=\"color:#006fa6;\">M054 </span><span style=\"color:#006fa6;\">M388 </span><span style='color:#bbb;'>, 6(N01)#</span><br /><span style=\"color:#006fa6;\">M288 </span><span style='color:#bbb;'>, 3(N01)#</span><br /><span style=\"color:#006fa6;\">M388 </span><span style=\"color:#006fa6;\">M145~A </span><span style=\"color:#006fa6;\">M372 </span><span style=\"color:#006fa6;\">|M153+M342| </span><span style='color:#bbb;'>, </span><br /><span style='color:#bbb;'>, 1(N34)#</span><br /></div><h3>P009220</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#006FA6;'>Topic 5: 91.82%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#006fa6;\">M157 </span><span style='color:#bbb;'>, </span><br /><span style=\"color:#006fa6;\">[M340] </span><span style=\"color:#006fa6;\">M054 </span><span style=\"color:#006fa6;\">M388 </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#006fa6;\">M288 </span><span style='color:#bbb;'>, 1(N01)# 2(N39B)# 1(N24)#</span><br /><span style=\"color:#006fa6;\">M388 </span><span style=\"color:#006fa6;\">M131 </span><span style=\"color:#006fa6;\">M218 </span><span style=\"color:#006fa6;\">|M153+M342| </span><span style='color:#bbb;'>, </span><br /><span style='color:#bbb;'>, [1(N34)]</span><br /></div><h1>Topic 6:</h1><hr/><p><h2>Representative terms:</h2><br/><span style='margin-left:2em;'>M218 (10.58%)</span><br/><span style='margin-left:2em;'>M388 (7.75%)</span><br/><span style='margin-left:2em;'>M371 (5.64%)</span><br/><span style='margin-left:2em;'>M066 (5.24%)</span><br/><span style='margin-left:2em;'>M096 (4.75%)</span><br/><span style='margin-left:2em;'>M004 (2.46%)</span><br/><span style='margin-left:2em;'>M263 (2.33%)</span><br/><span style='margin-left:2em;'>M057 (1.92%)</span><br/><span style='margin-left:2em;'>M219 (1.76%)</span><br/><span style='margin-left:2em;'>M347 (1.72%)</span></p><p><h2>Representative tablets:</h2><br/></p><h3>P008271</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#A30059;'>Topic 6: 99.09%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#a30059;\">M388 </span><span style='color:#bbb;'>, </span><br /><span style=\"color:#a30059;\">M338~B </span><span style=\"color:#a30059;\">M066 </span><span style=\"color:#a30059;\">M288 </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#a30059;\">M004 </span><span style=\"color:#a30059;\">M263~1 </span><span style=\"color:#a30059;\">M218 </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#a30059;\">M146 </span><span style=\"color:#a30059;\">M004 </span><span style=\"color:#a30059;\">M263~1 </span><span style=\"color:#a30059;\">M218 </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#a30059;\">M049~N </span><span style=\"color:#a30059;\">M240 </span><span style=\"color:#a30059;\">M219 </span><span style=\"color:#a30059;\">M387 </span><span style=\"color:#a30059;\">M218 </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#a30059;\">M263~1 </span><span style=\"color:#a30059;\">M381 </span><span style=\"color:#a30059;\">M371 </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#a30059;\">M033 </span><span style=\"color:#a30059;\">M371 </span><span style='color:#bbb;'>, 1(N14)</span><br /><span style=\"color:#a30059;\">M048~C </span><span style=\"color:#a30059;\">M096 </span><span style='color:#bbb;'>, 1(N14)</span><br /><span style=\"color:#a30059;\">M377~E </span><span style=\"color:#a30059;\">M347 </span><span style=\"color:#a30059;\">M146 </span><span style=\"color:#a30059;\">|M218+M288| </span><span style=\"color:#a30059;\">M218 </span><span style='color:#bbb;'>, 2(N14) 2(N01)</span><br /><span style=\"color:#a30059;\">M218 </span><span style=\"color:#a30059;\">M151~F </span><span style=\"color:#a30059;\">M096~D </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#a30059;\">M146 </span><span style=\"color:#a30059;\">M371 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#a30059;\">M332~D </span><span style=\"color:#a30059;\">M223~B </span><span style=\"color:#a30059;\">M218 </span><span style='color:#bbb;'>, 2(N01)#</span><br /><span style=\"color:#a30059;\">M124 </span><span style=\"color:#a30059;\">M386~A </span><span style=\"color:#a30059;\">M240 </span><span style=\"color:#a30059;\">M096 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#a30059;\">M285~K </span><span style=\"color:#a30059;\">M329 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#a30059;\">M386~A </span><span style=\"color:#a30059;\">M386~A </span><span style=\"color:#a30059;\">M066 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#000;\">X </span><span style=\"color:#000;\">X </span><span style=\"color:#a30059;\">M371 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#a30059;\">M218~B </span><span style=\"color:#a30059;\">M075~R </span><span style=\"color:#a30059;\">M066 </span><span style='color:#bbb;'>, 1(N14) 2(N01)</span><br /><span style=\"color:#a30059;\">M218 </span><span style=\"color:#000;\">X </span><span style=\"color:#a30059;\">M218 </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#a30059;\">M218 </span><span style=\"color:#a30059;\">M259 </span><span style=\"color:#a30059;\">M371 </span><span style=\"color:#a30059;\">M223~B </span><span style=\"color:#a30059;\">M218 </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#a30059;\">M254~A </span><span style=\"color:#a30059;\">M143 </span><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style=\"color:#000;\">X </span><span style=\"color:#a30059;\">|M296+M296| </span><span style=\"color:#a30059;\">M066 </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#a30059;\">M387~EF </span><span style=\"color:#a30059;\">M057 </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#a30059;\">M218 </span><span style=\"color:#a30059;\">M295~F </span><span style=\"color:#a30059;\">M240 </span><span style=\"color:#a30059;\">M096 </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#a30059;\">M254~A </span><span style=\"color:#a30059;\">M372~A </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style=\"color:#a30059;\">M371 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#a30059;\">M377~E </span><span style=\"color:#a30059;\">M075~R </span><span style=\"color:#a30059;\">M066 </span><span style='color:#bbb;'>, 1(N14)#</span><br /><span style=\"color:#a30059;\">M146~2 </span><span style=\"color:#a30059;\">M004 </span><span style=\"color:#a30059;\">M263 </span><span style=\"color:#a30059;\">M218 </span><span style='color:#bbb;'>, 2(N39B)</span><br /><span style=\"color:#a30059;\">M131 </span><span style=\"color:#a30059;\">M314~C </span><span style=\"color:#a30059;\">M128 </span><span style=\"color:#a30059;\">M096 </span><span style='color:#bbb;'>, 1(N39B)</span><br /><span style=\"color:#a30059;\">M146~2 </span><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...] 1(N39B)</span><br /><span style=\"color:#a30059;\">M024 </span><span style=\"color:#a30059;\">M390 </span><span style=\"color:#a30059;\">M218 </span><span style='color:#bbb;'>, 3(N39B)</span><br /><span style=\"color:#a30059;\">M371 </span><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, 2(N39B)</span><br /><span style=\"color:#a30059;\">M218 </span><span style=\"color:#a30059;\">M242~B </span><span style=\"color:#a30059;\">M096 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#a30059;\">M049~N </span><span style=\"color:#a30059;\">M251 </span><span style=\"color:#a30059;\">M066 </span><span style='color:#bbb;'>, 2(N01)#</span><br /><span style=\"color:#a30059;\">M288 </span><span style='color:#bbb;'>, 1(N45) 5(N14) 1(N01) 3(N39B)</span><br /></div><h3>P008241</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#A30059;'>Topic 6: 98.27%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, </span><br /><span style=\"color:#a30059;\">M387~HB </span><span style=\"color:#a30059;\">M388 </span><span style=\"color:#a30059;\">M057~A </span><span style=\"color:#a30059;\">M029~B </span><span style=\"color:#a30059;\">M057~A </span><span style=\"color:#a30059;\">|M217+M124| </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#a30059;\">|M217+M388| </span><span style='color:#bbb;'>, [1(N01)]</span><br /><span style=\"color:#a30059;\">M390 </span><span style=\"color:#a30059;\">M128 </span><span style=\"color:#a30059;\">M096 </span><span style=\"color:#a30059;\">|M217+M388| </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#a30059;\">M254~A </span><span style=\"color:#a30059;\">M058 </span><span style=\"color:#a30059;\">M058 </span><span style=\"color:#a30059;\">M057~A </span><span style=\"color:#a30059;\">|M217+M388| </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#a30059;\">M124 </span><span style=\"color:#a30059;\">M386~A </span><span style=\"color:#a30059;\">M386~A </span><span style=\"color:#a30059;\">M066 </span><span style=\"color:#a30059;\">|M217+M388| </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#a30059;\">M124 </span><span style=\"color:#a30059;\">M390 </span><span style=\"color:#a30059;\">M338~B </span><span style=\"color:#a30059;\">M066 </span><span style=\"color:#a30059;\">|M217+M388| </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#a30059;\">M240~E </span><span style=\"color:#a30059;\">M347 </span><span style=\"color:#a30059;\">M371 </span><span style=\"color:#a30059;\">|M217+M388| </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#a30059;\">M124 </span><span style=\"color:#a30059;\">M218 </span><span style=\"color:#a30059;\">M032 </span><span style=\"color:#a30059;\">M096 </span><span style=\"color:#a30059;\">|M217+M388| </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#a30059;\">M124 </span><span style=\"color:#a30059;\">M218 </span><span style=\"color:#a30059;\">M219 </span><span style=\"color:#a30059;\">M218 </span><span style=\"color:#a30059;\">|M217+M388| </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#a30059;\">M066 </span><span style=\"color:#a30059;\">M352~N </span><span style=\"color:#a30059;\">|M096+M139| </span><span style=\"color:#a30059;\">|M217+M388| </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#a30059;\">M218~B </span><span style=\"color:#a30059;\">M372 </span><span style=\"color:#a30059;\">|M217+M388| </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#000;\">X </span><span style=\"color:#000;\">X </span><span style=\"color:#a30059;\">|M217+M388| </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#000;\">[...] </span><span style=\"color:#a30059;\">|M217+M388| </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#a30059;\">|M217+M388| </span><span style=\"color:#a30059;\">|M217+M124| </span><span style='color:#bbb;'>, 1(N14) 7(N01)</span><br /></div><h3>P008144</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#A30059;'>Topic 6: 97.35%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#000;\">[...] </span><span style=\"color:#000;\">X </span><span style=\"color:#a30059;\">M102~D </span><span style=\"color:#a30059;\">M096 </span><span style='color:#bbb;'>, 1(N01) 1(N39B)</span><br /><span style=\"color:#a30059;\">M029~A </span><span style=\"color:#a30059;\">M009 </span><span style=\"color:#a30059;\">M371 </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#a30059;\">M377 </span><span style=\"color:#a30059;\">M386~A </span><span style=\"color:#a30059;\">M240 </span><span style=\"color:#a30059;\">M096 </span><span style='color:#bbb;'>, 2(N39B) 1(N24)</span><br /><span style=\"color:#a30059;\">M218 </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#a30059;\">M057~B </span><span style=\"color:#a30059;\">M099 </span><span style=\"color:#a30059;\">M371 </span><span style='color:#bbb;'>, 2(N01) 2(N39B)</span><br /><span style=\"color:#a30059;\">M218 </span><span style=\"color:#a30059;\">M004 </span><span style=\"color:#a30059;\">M218 </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#a30059;\">M332~D </span><span style=\"color:#a30059;\">M387~EF </span><span style=\"color:#a30059;\">M066 </span><span style='color:#bbb;'>, 2(N01) 3(N39B)</span><br /><span style=\"color:#a30059;\">M024 </span><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#a30059;\">M352~N </span><span style=\"color:#a30059;\">M097~H </span><span style=\"color:#a30059;\">M218 </span><span style='color:#bbb;'>, 1(N01) 4(N39B)</span><br /><span style=\"color:#a30059;\">M096 </span><span style='color:#bbb;'>, 1(N01) 2(N39B) 1(N24)</span><br /><span style=\"color:#a30059;\">M066 </span><span style=\"color:#a30059;\">M048~C </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 4(N39B)</span><br /><span style=\"color:#000;\">X </span><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#a30059;\">M024 </span><span style=\"color:#000;\">X </span><span style=\"color:#a30059;\">M066 </span><span style='color:#bbb;'>, 1(N01) 3(N39B)</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...] 1(N24)</span><br /><span style=\"color:#a30059;\">M263~1 </span><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, 2(N39B) 1(N24)</span><br /><span style=\"color:#a30059;\">M207~M </span><span style=\"color:#a30059;\">M001 </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#a30059;\">M288 </span><span style='color:#bbb;'>, 8(N14) 5(N01) 2(N39B)# [...]</span><br /></div><h1>Topic 7:</h1><hr/><p><h2>Representative terms:</h2><br/><span style='margin-left:2em;'>M009 (7.20%)</span><br/><span style='margin-left:2em;'>M106_M009 (5.13%)</span><br/><span style='margin-left:2em;'>M009_M206~G (3.84%)</span><br/><span style='margin-left:2em;'>M309~A (3.67%)</span><br/><span style='margin-left:2em;'>M106~A (3.54%)</span><br/><span style='margin-left:2em;'>M106 (3.48%)</span><br/><span style='margin-left:2em;'>M362_M367 (3.40%)</span><br/><span style='margin-left:2em;'>M206~G (3.38%)</span><br/><span style='margin-left:2em;'>M102~E_M309~A (3.05%)</span><br/><span style='margin-left:2em;'>M102~E (2.99%)</span></p><p><h2>Representative tablets:</h2><br/></p><h3>P008295</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#7A4900;'>Topic 7: 99.52%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#7a4900;\">[M362+X] </span><span style='color:#bbb;'>, 2(N14) 2(N01)</span><br /><span style=\"color:#7a4900;\">M269~A2 </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#7a4900;\">M106~A </span><span style='color:#bbb;'>, 3(N39B)# 1(N24) 1(N30C)</span><br /><span style=\"color:#7a4900;\">M009 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M206~G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M102~E </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">M309~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">[M362+X] </span><span style='color:#bbb;'>, 1(N14)# 7(N01)</span><br /><span style=\"color:#7a4900;\">M269~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M269~B </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M106~A </span><span style='color:#bbb;'>, 3(N39B)</span><br /><span style=\"color:#7a4900;\">M009 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M206~G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M102~E </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">M309~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">|M362+M059~D| </span><span style=\"color:#7a4900;\">|M001+M379~C| </span><span style='color:#bbb;'>, 5(N01)</span><br /><span style=\"color:#7a4900;\">[M269~A] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">[M269~B] </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M106~A </span><span style='color:#bbb;'>, 1(N24) 2(N30C)</span><br /><span style=\"color:#7a4900;\">M009 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M206~G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M102~E </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">M309~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">|M362+M059~D| </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">M260 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M106~A </span><span style='color:#bbb;'>, 2(N30C)</span><br /><span style=\"color:#7a4900;\">M009 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M206~G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M102~E </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">[M309~A] </span><span style='color:#bbb;'>, [1(N01)]</span><br /><span style=\"color:#7a4900;\">|M362+M207~N| </span><span style=\"color:#7a4900;\">M005 </span><span style='color:#bbb;'>, 5(N01)</span><br /><span style=\"color:#7a4900;\">M269~3 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M106~A </span><span style='color:#bbb;'>, 1(N24) 2(N30C)</span><br /><span style=\"color:#7a4900;\">M009 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M206~G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M102~E </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">M309~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">|M362+M099~B| </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#7a4900;\">M362~A </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#7a4900;\">M260 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M106 </span><span style='color:#bbb;'>, 1(N14) 6(N01)</span><br /><span style=\"color:#7a4900;\">M009 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M206~G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M102~E </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">M309~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">|M362+M244| </span><span style='color:#bbb;'>, 1(N14) 1(N01)</span><br /><span style=\"color:#7a4900;\">M269~3 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M260 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">[M009] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">[M206~G] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">[M102~E] </span><span style='color:#bbb;'>, 2(N01)#</span><br /><span style=\"color:#7a4900;\">M309~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">|M362+M158| </span><span style='color:#bbb;'>, 5(N01)</span><br /><span style=\"color:#7a4900;\">M260 </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#7a4900;\">M106 </span><span style='color:#bbb;'>, 5(N01)</span><br /><span style=\"color:#7a4900;\">M009 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M206~G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M102~E </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">M309~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">[M362+X] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#7a4900;\">M106 </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">M009 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M206~G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M102~E </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">M309~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M362 </span><span style=\"color:#7a4900;\">M383~C </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">M260 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M106 </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">M009 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M206~G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">[M102~E] </span><span style='color:#bbb;'>, [2(N01)]</span><br /><span style=\"color:#7a4900;\">[M309~A] </span><span style='color:#bbb;'>, [1(N01)]</span><br /><span style=\"color:#7a4900;\">|M362+M123~B| </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#7a4900;\">M260 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M106 </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#7a4900;\">M009 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M206~G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M102~E </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">M309~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M351~C </span><span style=\"color:#7a4900;\">M362 </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">[M009] </span><span style='color:#bbb;'>, [1(N01)]</span><br /><span style=\"color:#7a4900;\">[M206~G] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">[M102~E] </span><span style='color:#bbb;'>, [2(N01)]</span><br /><span style=\"color:#7a4900;\">[M309~A] </span><span style='color:#bbb;'>, [1(N01)]</span><br /><span style=\"color:#7a4900;\">|M362+M312| </span><span style='color:#bbb;'>, 3(N01)#</span><br /><span style=\"color:#7a4900;\">M269 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M106~A </span><span style='color:#bbb;'>, 1(N24) 2(N30C)</span><br /><span style=\"color:#7a4900;\">M009 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M206~G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M102~E </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">[M309~A] </span><span style='color:#bbb;'>, [1(N01)]</span><br /><span style=\"color:#7a4900;\">[|M362+X|] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">[M009] </span><span style='color:#bbb;'>, [1(N01)]</span><br /><span style=\"color:#7a4900;\">[M206~G] </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">M102~E </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M309~A </span><span style='color:#bbb;'>, [1(N01)]</span><br /><span style=\"color:#7a4900;\">M362~A </span><span style='color:#bbb;'>, 1(N14) 3(N01)</span><br /><span style=\"color:#7a4900;\">M260 </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#7a4900;\">M260 </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">M260 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M260 </span><span style='color:#bbb;'>, 8(N01)</span><br /><span style=\"color:#7a4900;\">M106 </span><span style='color:#bbb;'>, 4(N14) 2(N01)</span><br /><span style=\"color:#7a4900;\">M106~A </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">[M009] </span><span style='color:#bbb;'>, [1(N14) 4(N01)]</span><br /><span style=\"color:#7a4900;\">[M206~G] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">[M102~E] </span><span style='color:#bbb;'>, [2(N14)] 8(N01)! 6(N01)</span><br /><span style=\"color:#7a4900;\">M309~A </span><span style='color:#bbb;'>, 1(N14) 4(N01)</span><br /></div><h3>P008283</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#7A4900;'>Topic 7: 99.49%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, </span><br /><span style=\"color:#7a4900;\">[|M362+X|] </span><span style='color:#bbb;'>, [N(N01)]</span><br /><span style=\"color:#7a4900;\">[M260] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">[M106~A] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">M009 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M206~G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M102~E </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">M309~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">|M362+M384~A| </span><span style='color:#bbb;'>, 6(N01)</span><br /><span style=\"color:#7a4900;\">M269 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">[M106~A] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">[M009] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">M206~G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M102~E </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">M309~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">|M362+M059| </span><span style=\"color:#7a4900;\">|M001+M379~C| </span><span style='color:#bbb;'>, 5(N01)</span><br /><span style=\"color:#7a4900;\">M269~1 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M106~A </span><span style='color:#bbb;'>, 1(N24) 2(N30C)</span><br /><span style=\"color:#7a4900;\">M009 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M206~G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M102~E </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">[M309~A] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">[|M362+X|] </span><span style='color:#bbb;'>, 8(N01)#</span><br /><span style=\"color:#7a4900;\">M269 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M106~A </span><span style='color:#bbb;'>, 1(N39B) 2(N30C)</span><br /><span style=\"color:#7a4900;\">M009 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M206~G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M102~E </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">M309~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M207~N </span><span style=\"color:#7a4900;\">|M362+M041~E| </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#7a4900;\">M260 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M106~A </span><span style='color:#bbb;'>, 1(N24)</span><br /><span style=\"color:#7a4900;\">M009 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">[M206~G] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">[M102~E] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">[M309~A] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style=\"color:#7a4900;\">|M362+M099~B| </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#7a4900;\">M367~A </span><span style='color:#bbb;'>, 6(N01)</span><br /><span style=\"color:#7a4900;\">M269 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M106 </span><span style='color:#bbb;'>, 9(N01)</span><br /><span style=\"color:#7a4900;\">M009 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M206~G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M102~E </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">M309~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">|M362+X| </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">[M269] </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#7a4900;\">M106 </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#7a4900;\">M009 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M206~G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M102~E </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">M309~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">|M362+M244| </span><span style='color:#bbb;'>, 7(N01)</span><br /><span style=\"color:#7a4900;\">M269 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M106 </span><span style='color:#bbb;'>, 7(N01)</span><br /><span style=\"color:#7a4900;\">M009 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">[M206~G] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">[M102~E] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">[M309~A] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">|M362+M158| </span><span style='color:#bbb;'>, 5(N01)</span><br /><span style=\"color:#7a4900;\">M269~1 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M106 </span><span style='color:#bbb;'>, 5(N01)</span><br /><span style=\"color:#7a4900;\">M009 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M206~G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M102~E </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">M309~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">|M362+M026~H| </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">M260 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M106 </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">[M009] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">[M206~G] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">[M102~E] </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#7a4900;\">M309~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">|M362+M312~A| </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#7a4900;\">M367~A </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">M269 </span><span style='color:#bbb;'>, 1(N01) 1(N24) 2(N30C)</span><br /><span style=\"color:#7a4900;\">M009 </span><span style='color:#bbb;'>, 1(N91)</span><br /><span style=\"color:#7a4900;\">M206~G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M102~E </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">M309~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">|M362+M383~C| </span><span style='color:#bbb;'>, 4(N01)</span><br /><span style=\"color:#7a4900;\">M367~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">[M269] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">[M106] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">[M009] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">[M206~G] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">M102~E </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">M309~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">|M362+M123~B| </span><span style='color:#bbb;'>, 4(N01)</span><br /><span style=\"color:#7a4900;\">M260 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M106 </span><span style='color:#bbb;'>, 4(N01)</span><br /><span style=\"color:#7a4900;\">M009 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M206~G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M102~E </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">M309~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">[|M362+X|] </span><span style='color:#bbb;'>, [N(N01)]</span><br /><span style=\"color:#7a4900;\">[M269] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">[M106] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">M009 </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#7a4900;\">M206~G </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M102~E </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#7a4900;\">M309~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M362 </span><span style='color:#bbb;'>, 6(N14) 5(N01)</span><br /><span style=\"color:#7a4900;\">M362~A </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#7a4900;\">M106~A </span><span style='color:#bbb;'>, 1(N01) 1(N39B) 1(N24)</span><br /><span style=\"color:#7a4900;\">[M309~A] </span><span style='color:#bbb;'>, [1(N14)] 4(N01)</span><br /></div><h3>P393084</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#7A4900;'>Topic 7: 87.14%</span><br/><span style='margin-left:2em;color:#006FA6;'>Topic 5: 1.43%</span><br/><span style='margin-left:2em;color:#63FFAC;'>Topic 9: 1.43%</span><br/><span style='margin-left:2em;color:#FF4A46;'>Topic 3: 1.43%</span><br/><span style='margin-left:2em;color:#FF34FF;'>Topic 2: 1.43%</span><br/><span style='margin-left:2em;color:#1CE6FF;'>Topic 1: 1.43%</span><br/><span style='margin-left:2em;color:#008941;'>Topic 4: 1.43%</span><br/><span style='margin-left:2em;color:#A30059;'>Topic 6: 1.43%</span><br/><span style='margin-left:2em;color:#0000A6;'>Topic 8: 1.43%</span><br/><span style='margin-left:2em;color:#B79762;'>Topic 10: 1.43%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#7a4900;\">MXXX </span><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M157~A </span><span style=\"color:#7a4900;\">M317+MXXX </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style=\"color:#7a4900;\">MXXX </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#7a4900;\">M351+MXXX </span><span style=\"color:#7a4900;\">M102~A </span><span style='color:#bbb;'>, 1(N01)#</span><br /></div><h1>Topic 8:</h1><hr/><p><h2>Representative terms:</h2><br/><span style='margin-left:2em;'>M157_|M195+M057| (6.91%)</span><br/><span style='margin-left:2em;'>|M195+M057| (3.75%)</span><br/><span style='margin-left:2em;'>M269~B (3.44%)</span><br/><span style='margin-left:2em;'>M157 (3.39%)</span><br/><span style='margin-left:2em;'>M153 (3.21%)</span><br/><span style='margin-left:2em;'>M009 (2.29%)</span><br/><span style='margin-left:2em;'>M145 (1.99%)</span><br/><span style='margin-left:2em;'>M388 (1.54%)</span><br/><span style='margin-left:2em;'>M265 (1.44%)</span><br/><span style='margin-left:2em;'>|M377+M377| (1.39%)</span></p><p><h2>Representative tablets:</h2><br/></p><h3>P008315</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#0000A6;'>Topic 8: 97.43%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#0000a6;\">[M157] </span><span style='color:#bbb;'>, </span><br /><span style=\"color:#0000a6;\">M205~G </span><span style=\"color:#0000a6;\">M278~E </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#0000a6;\">M343~D </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#0000a6;\">M317~A </span><span style=\"color:#0000a6;\">M264~D </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#0000a6;\">M283~A </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#0000a6;\">M421 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#0000a6;\">M145 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#0000a6;\">M207~D </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#0000a6;\">M081~1 </span><span style=\"color:#0000a6;\">M135 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#0000a6;\">M038~B </span><span style=\"color:#0000a6;\">M145 </span><span style='color:#bbb;'>, 4(N01)</span><br /><span style=\"color:#0000a6;\">M207~D </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#0000a6;\">M135 </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#0000a6;\">M458~B </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#0000a6;\">|M305+M342| </span><span style=\"color:#0000a6;\">M081 </span><span style=\"color:#0000a6;\">M228~GA </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#0000a6;\">M145 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#0000a6;\">M149~H </span><span style='color:#bbb;'>, 5(N01)#</span><br /><span style=\"color:#0000a6;\">M090 </span><span style=\"color:#0000a6;\">M145 </span><span style='color:#bbb;'>, 1(N08A)</span><br /><span style=\"color:#0000a6;\">|M157+M340| </span><span style=\"color:#0000a6;\">M145 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#0000a6;\">M149~H </span><span style='color:#bbb;'>, 2(N01)#</span><br /><span style=\"color:#0000a6;\">M264~D </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#0000a6;\">M145 </span><span style='color:#bbb;'>, 7(N01)# [1(N08A)]</span><br /><span style=\"color:#0000a6;\">M149~H </span><span style='color:#bbb;'>, 7(N01)#</span><br /><span style=\"color:#0000a6;\">M207~D </span><span style='color:#bbb;'>, 3(N01)#</span><br /><span style=\"color:#0000a6;\">M081 </span><span style=\"color:#0000a6;\">[M135] </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#0000a6;\">M343~D </span><span style='color:#bbb;'>, 1(N01)</span><br /></div><h3>P008233</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#0000A6;'>Topic 8: 94.00%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#0000a6;\">M263~G </span><span style='color:#bbb;'>, 1(N14)</span><br /><span style=\"color:#0000a6;\">M283~J </span><span style='color:#bbb;'>, 7(N01)</span><br /><span style=\"color:#0000a6;\">M269~B </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#0000a6;\">M054~I </span><span style=\"color:#0000a6;\">M268~C </span><span style='color:#bbb;'>, 5(N01)</span><br /><span style=\"color:#0000a6;\">M268~B </span><span style='color:#bbb;'>, 4(N01)</span><br /><span style=\"color:#0000a6;\">M269~B </span><span style='color:#bbb;'>, 1(N14) 1(N01)</span><br /><span style=\"color:#0000a6;\">M210~M </span><span style=\"color:#0000a6;\">M268~M </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#0000a6;\">M268~C </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#0000a6;\">M268~B </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#000;\">[...] </span><span style=\"color:#0000a6;\">M268~C </span><span style='color:#bbb;'>, 6(N01)</span><br /><span style=\"color:#0000a6;\">M268~B </span><span style='color:#bbb;'>, 6(N01)#</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#0000a6;\">M269~B </span><span style='color:#bbb;'>, 3(N14) 8(N01)</span><br /></div><h3>P009272</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#0000A6;'>Topic 8: 88.75%</span><br/><span style='margin-left:2em;color:#006FA6;'>Topic 5: 1.25%</span><br/><span style='margin-left:2em;color:#A30059;'>Topic 6: 1.25%</span><br/><span style='margin-left:2em;color:#7A4900;'>Topic 7: 1.25%</span><br/><span style='margin-left:2em;color:#B79762;'>Topic 10: 1.25%</span><br/><span style='margin-left:2em;color:#008941;'>Topic 4: 1.25%</span><br/><span style='margin-left:2em;color:#FF4A46;'>Topic 3: 1.25%</span><br/><span style='margin-left:2em;color:#FF34FF;'>Topic 2: 1.25%</span><br/><span style='margin-left:2em;color:#1CE6FF;'>Topic 1: 1.25%</span><br/><span style='margin-left:2em;color:#63FFAC;'>Topic 9: 1.25%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#0000a6;\">M157 </span><span style='color:#bbb;'>, </span><br /><span style=\"color:#0000a6;\">|M195+M057| </span><span style=\"color:#0000a6;\">M388 </span><span style=\"color:#0000a6;\">M009 </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /></div><h1>Topic 9:</h1><hr/><p><h2>Representative terms:</h2><br/><span style='margin-left:2em;'>M387 (7.19%)</span><br/><span style='margin-left:2em;'>M036 (5.49%)</span><br/><span style='margin-left:2em;'>M354 (5.43%)</span><br/><span style='margin-left:2em;'>M297 (4.16%)</span><br/><span style='margin-left:2em;'>|M036+1(N30D)| (3.80%)</span><br/><span style='margin-left:2em;'>M263~A (2.84%)</span><br/><span style='margin-left:2em;'>M263 (2.66%)</span><br/><span style='margin-left:2em;'>M001 (1.78%)</span><br/><span style='margin-left:2em;'>M387~A (1.73%)</span><br/><span style='margin-left:2em;'>M387~EF (1.66%)</span></p><p><h2>Representative tablets:</h2><br/></p><h3>P009048</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#63FFAC;'>Topic 9: 96.54%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#63ffac;\">|M351+X| </span><span style='color:#bbb;'>, 1(N54)</span><br /><span style=\"color:#63ffac;\">M222 </span><span style='color:#bbb;'>, 1(N34) 2(N01)</span><br /><span style=\"color:#63ffac;\">|M327+M365| </span><span style=\"color:#63ffac;\">M354 </span><span style='color:#bbb;'>, 2(N51) 2(N14) 6(N01)</span><br /><span style=\"color:#000;\"> </span><span style='color:#bbb;'>, 1(N51@B)</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 4(N01)</span><br /><span style=\"color:#63ffac;\">|M351+X| </span><span style='color:#bbb;'>, 1(N51) 1(N34) 2(N14) 2(N01)</span><br /><span style=\"color:#63ffac;\">|M351+X| </span><span style=\"color:#63ffac;\">M354 </span><span style='color:#bbb;'>, 1(N51) 4(N14)</span><br /><span style=\"color:#000;\"> </span><span style='color:#bbb;'>, 3(N51@B) 5(N14@B)</span><br /><span style=\"color:#63ffac;\">|M351+X| </span><span style='color:#bbb;'>, 1(N51) 5(N14)</span><br /><span style=\"color:#63ffac;\">M222 </span><span style='color:#bbb;'>, 6(N14)</span><br /><span style=\"color:#63ffac;\">M327 </span><span style=\"color:#63ffac;\">M354 </span><span style='color:#bbb;'>, 2(N51) 2(N34)</span><br /><span style=\"color:#63ffac;\">M352~H </span><span style='color:#bbb;'>, 4(N51) 1(N34) 5(N14) 8(N01)</span><br /><span style=\"color:#63ffac;\">M223 </span><span style='color:#bbb;'>, 4(N14) 3(N01)</span><br /><span style=\"color:#63ffac;\">|M327+M348| </span><span style=\"color:#63ffac;\">M354 </span><span style='color:#bbb;'>, 7(N51) 1(N34) 5(N14) 5(N01)</span><br /><span style=\"color:#63ffac;\">M352~H </span><span style='color:#bbb;'>, 3(N51)</span><br /><span style=\"color:#63ffac;\">M327 </span><span style=\"color:#63ffac;\">M354 </span><span style='color:#bbb;'>, 2(N51) 1(N34) 4(N14) 2(N01)</span><br /><span style=\"color:#000;\"> </span><span style='color:#bbb;'>, 3(N51@B) 1(N34@B) 4(N14@B) 1(N01@B)</span><br /><span style=\"color:#63ffac;\">|M351+X| </span><span style='color:#bbb;'>, 5(N51)</span><br /><span style=\"color:#63ffac;\">M223 </span><span style='color:#bbb;'>, 4(N14) 5(N01) 2(N39B)</span><br /><span style=\"color:#63ffac;\">M327 </span><span style=\"color:#63ffac;\">M354 </span><span style='color:#bbb;'>, 2(N51) 4(N14) 6(N01)</span><br /><span style=\"color:#000;\"> </span><span style='color:#bbb;'>, 2(N1@B)</span><br /><span style=\"color:#63ffac;\">|M351+X| </span><span style='color:#bbb;'>, 2(N51) 4(N14) 5(N01)</span><br /><span style=\"color:#63ffac;\">M223 </span><span style='color:#bbb;'>, 5(N14) 2(N01)</span><br /><span style=\"color:#63ffac;\">M354 </span><span style='color:#bbb;'>, 1(N54) 7(N51) 4(N14) 4(N01)</span><br /><span style=\"color:#000;\"> </span><span style='color:#bbb;'>, 2(N54@B) 1(N51@B) 1(N34@B) 1(N14@B)</span><br /><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, 2(N51)#</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 8(N51) 5(N14) 5(N01)</span><br /></div><h3>P008640</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#63FFAC;'>Topic 9: 94.72%</span><br/><span style='margin-left:2em;color:#FF4A46;'>Topic 3: 3.05%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#63ffac;\">[M387] </span><span style=\"color:#63ffac;\">M069~A </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#63ffac;\">M387 </span><span style=\"color:#63ffac;\">M069~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#63ffac;\">M387 </span><span style=\"color:#63ffac;\">M069~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#63ffac;\">M387 </span><span style=\"color:#63ffac;\">M069~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#63ffac;\">M387 </span><span style=\"color:#63ffac;\">M069~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#63ffac;\">M387 </span><span style=\"color:#63ffac;\">M069~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#63ffac;\">M387 </span><span style=\"color:#63ffac;\">M069~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#63ffac;\">[M387 </span><span style=\"color:#63ffac;\">M069~A] </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#63ffac;\">M387 </span><span style=\"color:#63ffac;\">M069~A </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#63ffac;\">M387 </span><span style=\"color:#63ffac;\">M069~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#63ffac;\">M387 </span><span style=\"color:#63ffac;\">M069~A </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#ff4a46;\">M038~A </span><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style=\"color:#63ffac;\">M112~E </span><span style='color:#bbb;'>, 3(N39B)</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /></div><h3>P008847</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#63FFAC;'>Topic 9: 93.08%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#63ffac;\">M387 </span><span style='color:#bbb;'>, </span><br /><span style=\"color:#63ffac;\">M054 </span><span style=\"color:#63ffac;\">M354 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#63ffac;\">M059 </span><span style='color:#bbb;'>, 2(N14)</span><br /><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#63ffac;\">M327 </span><span style='color:#bbb;'>, 4(N14)</span><br /><span style=\"color:#63ffac;\">M054 </span><span style='color:#bbb;'>, 2(N14)</span><br /><span style=\"color:#000;\">X </span><span style=\"color:#63ffac;\">M054 </span><span style='color:#bbb;'>, 4(N01)</span><br /><span style=\"color:#63ffac;\">M006 </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, 1(N14) 1(N14@B)</span><br /><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, 2(N01)#</span><br /><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 5(N1@B)</span><br /><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#63ffac;\">M157~A </span><span style=\"color:#000;\">X </span><span style=\"color:#63ffac;\">M354 </span><span style='color:#bbb;'>, 1(N14)</span><br /><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, 3(N14)</span><br /><span style=\"color:#63ffac;\">M297~C </span><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#63ffac;\">M354 </span><span style='color:#bbb;'>, 2(N51) 1(N34) 1(N14)# 5(N01)</span><br /></div><h1>Topic 10:</h1><hr/><p><h2>Representative terms:</h2><br/><span style='margin-left:2em;'>M124 (9.14%)</span><br/><span style='margin-left:2em;'>M054 (7.89%)</span><br/><span style='margin-left:2em;'>M288 (7.88%)</span><br/><span style='margin-left:2em;'>M370 (4.80%)</span><br/><span style='margin-left:2em;'>M072 (4.19%)</span><br/><span style='margin-left:2em;'>M003~B (3.75%)</span><br/><span style='margin-left:2em;'>M388 (3.29%)</span><br/><span style='margin-left:2em;'>M373 (2.66%)</span><br/><span style='margin-left:2em;'>M371 (2.34%)</span><br/><span style='margin-left:2em;'>M376 (1.99%)</span></p><p><h2>Representative tablets:</h2><br/></p><h3>P008432</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#B79762;'>Topic 10: 97.91%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#000;\">X </span><span style=\"color:#b79762;\">M124 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">M041 </span><span style=\"color:#b79762;\">M124 </span><span style=\"color:#b79762;\">M001 </span><span style=\"color:#000;\">X </span><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style=\"color:#b79762;\">M003~B </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">M123~B </span><span style=\"color:#b79762;\">M388 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">M370 </span><span style=\"color:#000;\">X </span><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#b79762;\">M218 </span><span style=\"color:#b79762;\">M281~F </span><span style=\"color:#b79762;\">M352~O </span><span style=\"color:#b79762;\">M387 </span><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style=\"color:#b79762;\">M175 </span><span style=\"color:#b79762;\">M046 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">M388 </span><span style=\"color:#b79762;\">M332~F </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#000;\">[...] </span><span style=\"color:#b79762;\">M066 </span><span style=\"color:#b79762;\">M066 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">M376 </span><span style=\"color:#b79762;\">M001 </span><span style=\"color:#b79762;\">M124 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">M048~C </span><span style=\"color:#b79762;\">M124 </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#000;\">[...] </span><span style=\"color:#b79762;\">M370 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">|M175+M131~D| </span><span style=\"color:#b79762;\">M388 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">M054 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">M214~I </span><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#b79762;\">M370 </span><span style=\"color:#b79762;\">M376 </span><span style=\"color:#b79762;\">M124 </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#000;\">X </span><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#000;\">X </span><span style=\"color:#000;\">X </span><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">M370 </span><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#b79762;\">M046 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">M254~B1 </span><span style=\"color:#b79762;\">M046 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">M309~D </span><span style=\"color:#b79762;\">M373~A </span><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#b79762;\">M003~B </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">M370 </span><span style=\"color:#b79762;\">M072 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">|M370+M388+M370| </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">M370 </span><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /></div><h3>P368479</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#B79762;'>Topic 10: 96.79%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#b79762;\">M123~B </span><span style=\"color:#b79762;\">M054 </span><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">X </span><span style=\"color:#b79762;\">M203~A </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#000;\">X </span><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, 1(N39B)#</span><br /><span style=\"color:#b79762;\">M046 </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#000;\">X </span><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#b79762;\">M370 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">M009 </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#b79762;\">M203~A </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">M046 </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#000;\">X </span><span style=\"color:#b79762;\">M054 </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 2(N01)#</span><br /><span style=\"color:#b79762;\">M288 </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#000;\">[...] </span><span style=\"color:#b79762;\">M370 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">|M370+M388+M370| </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">|M370~B+M072| </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 2(N01)#</span><br /><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#b79762;\">M370 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">M373 </span><span style='color:#bbb;'>, 3(N01)</span><br /><span style=\"color:#b79762;\">|M370+M072+M370| </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">|M370+X+M370| </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#b79762;\">M332~D </span><span style=\"color:#b79762;\">M066 </span><span style=\"color:#b79762;\">M054 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">M370 </span><span style=\"color:#b79762;\">M373 </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#000;\">[...] </span><span style=\"color:#000;\">X </span><span style=\"color:#b79762;\">M347 </span><span style=\"color:#b79762;\">M371 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">M370 </span><span style=\"color:#b79762;\">M124 </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">M203~A </span><span style='color:#bbb;'>, 1(N01)</span><br /></div><h3>P008104</h3><b>Topic composition:</b><br/><span style='margin-left:2em;color:#B79762;'>Topic 10: 95.00%</span><br/><b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\"><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, </span><br /><span style=\"color:#b79762;\">M329 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">M370 </span><span style=\"color:#b79762;\">M376 </span><span style=\"color:#b79762;\">M072 </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#b79762;\">|M157+M057| </span><span style=\"color:#b79762;\">M297 </span><span style=\"color:#b79762;\">M072 </span><span style='color:#bbb;'>, [...]</span><br /><span style=\"color:#b79762;\">M370 </span><span style=\"color:#b79762;\">M046 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">M388 </span><span style=\"color:#b79762;\">M376 </span><span style=\"color:#b79762;\">M001 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">|M370~B+M388| </span><span style='color:#bbb;'>, 1(N01)#</span><br /><span style=\"color:#b79762;\">M288 </span><span style='color:#bbb;'>, 1(N01) 2(N39B)</span><br /><span style=\"color:#000;\">[...] </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">M124 </span><span style='color:#bbb;'>, 1(N01)</span><br /><span style=\"color:#b79762;\">|M370+M388+M370| </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#b79762;\">|M370~B+M388| </span><span style='color:#bbb;'>, 2(N01)</span><br /><span style=\"color:#000;\">X </span><span style='color:#bbb;'>, 1(N01)# 2(N39B)#</span><br /></div></body></html>"
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   "source": [
    "########################################\n",
    "# This determines how many tablets are\n",
    "# printed out for each topic in the model:\n",
    "NUM_REPRESENTATIVE_TABLETS = 3\n",
    "\n",
    "def get_dominant_topics( ldamodel, corpus ):\n",
    "\n",
    "    dom_topics = []\n",
    "    for doc_no, topics in enumerate( ldamodel[corpus] ):\n",
    "        topics = sorted( topics, key=lambda x:-x[1] )\n",
    "        for j, ( topic_no, proportion ) in enumerate( topics ):\n",
    "            if j > 0:\n",
    "                continue # Only get dominant topic.\n",
    "            dom_topics.append(\n",
    "                [\n",
    "                    doc_no, \n",
    "                    int(topic_no)+1, \n",
    "                    proportion, \n",
    "                    tablets_with_real_content[doc_no].cdliNo\n",
    "                ]\n",
    "            )\n",
    "    return dom_topics\n",
    "\n",
    "from IPython.display import Image\n",
    "from numpy import exp\n",
    "from IPython.core.display import HTML, Image\n",
    "\n",
    "def topic_colors(t):\n",
    "    colors = [\"#1CE6FF\", \"#FF34FF\", \"#FF4A46\", \"#008941\", \"#006FA6\", \"#A30059\", \"#7A4900\",\n",
    "              \"#0000A6\", \"#63FFAC\", \"#B79762\", \"#004D43\", \"#8FB0FF\", \"#997D87\", \"#5A0007\", \n",
    "              \"#809693\", \"#FEFFE6\", \"#1B4400\", \"#4FC601\", \"#3B5DFF\", \"#4A3B53\", \"#FF2F80\",\n",
    "              \"#61615A\", \"#BA0900\", \"#6B7900\", \"#00C2A0\", \"#FFAA92\", \"#FF90C9\", \"#B903AA\", \n",
    "              \"#D16100\", \"#DDEFFF\", \"#000035\", \"#7B4F4B\", \"#A1C299\", \"#300018\", \"#0AA6D8\", \n",
    "              \"#013349\", \"#00846F\", \"#372101\", \"#FFB500\", \"#C2FFED\", \"#A079BF\", \"#CC0744\", \n",
    "              \"#C0B9B2\", \"#C2FF99\", \"#001E09\", \"#00489C\", \"#6F0062\", \"#0CBD66\", \"#EEC3FF\", \n",
    "              \"#456D75\", \"#B77B68\", \"#7A87A1\", \"#788D66\", \"#885578\", \"#FAD09F\", \"#FF8A9A\"]\n",
    "    return colors[t]\n",
    "\n",
    "def color_words(model, doc, text):\n",
    "    import matplotlib.pyplot as plt\n",
    "    import matplotlib.patches as patches\n",
    "    \n",
    "    doc_topics, word_topics, phi_values = model.get_document_topics(doc, per_word_topics=True)\n",
    "    \n",
    "    word2id = {model.id2word[w]:w for w in model.id2word}\n",
    "    html = ''\n",
    "    for word in text:\n",
    "        word_ = word\n",
    "        word = word.replace(\"[\",\"\").replace(\"]\",\"\")\n",
    "        if word not in word2id:\n",
    "            html += '<span style=\"color:#000;\">'+word_+' </span>'\n",
    "            continue\n",
    "        phis = [(t,) for t in phi_values if t[0] == word2id[word]][0]\n",
    "        best_topic = max(phis[0][1], key=lambda x:x[1])[0] if len(phis[0][1])>0 else -1\n",
    "        best_topic_phi = max(phis[0][1], key=lambda x:x[1])[1] if len(phis[0][1])>0 else 0\n",
    "        color = topic_colors(best_topic)\n",
    "        color = [ int(color[1:3],16), int(color[3:5],16), int(color[5:7],16) ]\n",
    "        scale_factor = exp(best_topic_phi) / (1+exp(best_topic_phi))\n",
    "        scale_factor = 1\n",
    "        color = [int(scale_factor*c) for c in color]\n",
    "        color = '#%02x%02x%02x'%tuple(color)\n",
    "        html += '<span style=\"color:'+color+';\">'+word_+' </span>'\n",
    "    return html\n",
    "\n",
    "def show_tablet( index ):\n",
    "    for i, row in enumerate(lda_model[corpus]):\n",
    "        if i != index:\n",
    "            continue\n",
    "        row = sorted(row, key=lambda x: -x[1])\n",
    "        t = tablets_with_real_content[index]\n",
    "        \n",
    "        html = '<div style=\"overflow:auto; margin-left:2em; max-width:1000px;\">'\n",
    "        for line in t.getLines(excludeNumeric=False):\n",
    "            if line[0] is not None:\n",
    "                signs=line[0].replace(\"?\",\"\").replace('#','').upper()\n",
    "                html += color_words(lda_model, corpus[i], \n",
    "                        signs.split(\" \")\n",
    "                       )\n",
    "            html += \"<span style='color:#bbb;'>, \"+(line[1] if line[1] is not None else '')+\"</span><br />\"\n",
    "        html += \"</div>\"\n",
    "        return html\n",
    "        \n",
    "        \n",
    "print(\"Formatting topic data...\")\n",
    "dominant_topics = get_dominant_topics( lda_model, corpus )\n",
    "dominant_topics = sorted(dominant_topics,key=lambda x:x[1])\n",
    "dominant_topics = [ (i+1, [row for row in dominant_topics if row[1]==i+1]) \n",
    "                   for i in range( lda_model.num_topics ) ]\n",
    "\n",
    "html = '<html><body>' \n",
    "\n",
    "for i, grp in dominant_topics:\n",
    "    html+=\"<h1>Topic %d:</h1><hr/>\"%(i)\n",
    "    wp = lda_model.show_topic(int(i-1))\n",
    "    html+=\"<p><h2>Representative terms:</h2><br/>\" +(\"<br/>\".join([\"<span style='margin-left:2em;'>%s (%.2f%%)</span>\"%(word,100*prop) for (word, prop) in wp]))+\"</p>\"\n",
    "    html+=\"<p><h2>Representative tablets:</h2><br/></p>\"\n",
    "    representative_tablets = sorted(grp,key=lambda x:-x[2])[:NUM_REPRESENTATIVE_TABLETS]\n",
    "    for row in representative_tablets:\n",
    "        index = row[0]\n",
    "        index = int(index)\n",
    "        html+=\"<h3>\"+row[3]+\"</h3>\"\n",
    "        html+=\"<b>Topic composition:</b><br/>\"\n",
    "        for t_num, perc_cont in sorted(lda_model[corpus][index],key=lambda x:-x[1]):\n",
    "            html+=\"<span style='margin-left:2em;color:%s;'>Topic %d: %.2f%%</span><br/>\"%(topic_colors(t_num),t_num+1,100*perc_cont)\n",
    "        html+=\"<b>Transliteration</b><br/>Words are colored according to the topic they most strongly represent.\"\n",
    "        html += show_tablet(index)\n",
    "\n",
    "html+=\"</body></html>\"\n",
    "# Save HTML to separate file for easier viewing:\n",
    "# outfile = open(\"LDA_report.html\",\"w\")\n",
    "# outfile.write(html)\n",
    "# outfile.close()\n",
    "display(HTML(html))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using these topic summaries in conjunction with the visualization above, we are able to interpret many of the learned topics; these interpretations are summarized in Born et al. 2019. Some topics, such as topic 5, have no clear interpretation based on our current understanding of proto-Elamite. It is possible that this topic is merely a collection of leftover material that does not fit nicely into any of the other topics. However, a cursory examination of the representative tablets for topic 5 suggests that many are sealed, and closer analysis confirms this to be the case:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 1:\t 12 sealed\t(11.76%)\n",
      "Topic 2:\t 21 sealed\t(12.00%)\n",
      "Topic 3:\t 7 sealed\t(8.24%)\n",
      "Topic 4:\t 9 sealed\t(7.96%)\n",
      "Topic 5:\t 31 sealed\t(21.53%)\n",
      "Topic 6:\t 38 sealed\t(12.58%)\n",
      "Topic 7:\t 5 sealed\t(9.43%)\n",
      "Topic 8:\t 9 sealed\t(9.78%)\n",
      "Topic 9:\t 14 sealed\t(6.90%)\n",
      "Topic 10:\t 5 sealed\t(3.85%)\n"
     ]
    }
   ],
   "source": [
    "sealed = defaultdict(int)\n",
    "total  = defaultdict(int)\n",
    "\n",
    "for topic_no,topic_tablets in dominant_topics:\n",
    "    for index,_,_,_ in topic_tablets:\n",
    "        t = tablets_with_real_content[index]\n",
    "        if t.sealed:\n",
    "            sealed[topic_no] += 1\n",
    "        total[topic_no] += 1\n",
    "\n",
    "for i in range(1,10+1):\n",
    "    print(\"Topic %d:\\t %d sealed\\t(%.2f%%)\"%(int(i),sealed[i],100*sealed[i]/total[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of the tablets where topic 5 is the dominant topic, 22% are sealed; topic 6 has the next-highest proportion of seals at just 13%. Although seals are recorded in the CDLI transliterations, `pype` removes these annotations when loading the corpus, and the LDA model has no information about their presence or absence. The fact that topic 5 has such a high number of sealed texts may therefore indicate that the LDA model is picking up on some similarity between these texts which we are not currently aware of. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.3 Topic Stability\n",
    "\n",
    "LDA is a probabilistic algorithm, and it may return different topics when run multiple times on the same data. [Mäntylä et al. (2018)](https://arxiv.org/pdf/1808.08098.pdf) provide a technique for finding stable topics by looking for similarities across multiple executions of the algorithm. \n",
    "\n",
    "These authors suggest learning a model with $t$ topics $k$ times to get $t*k$ topic-term vectors. Running $k$-medoids on the topic-term vectors produces $t$ topic clusters. The stability of a cluster is defined as the rank-biased overlap (RBO) between all vectors in that cluster:\n",
    "\n",
    "$$RBO(T1, T2, p, d) = \\frac{X_d}{d}\\cdot p^d + \\frac{1-p}{p}\\sum_{i=1}^{d}\\frac{X_i}{i}\\cdot p^i$$\n",
    "\n",
    "where T1 and T2 are two ranked lists (topic-term vectors), $d$ is the evaluation depth (evaluation only considers the $d$ most predictive words for each topic), and $X_d$ is the size of the intersection of the first $d$ entries in T1 with the first $d$ entries in T2. $p$ determines the degree to which order is taken into account. $p=1$ ignores order; as $p$ decreases, more weight is assigned to terms which are more highly predictive. The authors suggest $p=0.9$ \n",
    "\n",
    "The following code applies Mäntylä et al.'s method to our data to determine which topics are stable. For reference, output from the particular run discussed in Born et al. 2019 is included in `saved_models/stability.txt`. In order to make this code block run in a reasonable amount of time, we limit it to averaging over 30 models; you may adjust this by editing `n_runs`. It remains for future work to determine which topics are most stable in the limit, by increasing `n_runs` until the set of stable topics stops changing between runs. \n",
    "\n",
    "**Even limited to 30 models, this code block can be very slow.** You can safely skip it, as no later code depends on anything computed here. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training 30 LDA models...\n",
      "\tModel 0: seed 1512468269\n",
      "\tModel 1: seed 1542404754\n",
      "\tModel 2: seed 462947820\n",
      "\tModel 3: seed 2812911482\n",
      "\tModel 4: seed 1481665161\n",
      "\tModel 5: seed 2964920013\n",
      "\tModel 6: seed 215036720\n",
      "\tModel 7: seed 2472806397\n",
      "\tModel 8: seed 487289074\n",
      "\tModel 9: seed 1343248740\n",
      "\tModel 10: seed 1187839550\n",
      "\tModel 11: seed 4138433783\n",
      "\tModel 12: seed 3116687084\n",
      "\tModel 13: seed 2566676960\n",
      "\tModel 14: seed 1468936248\n",
      "\tModel 15: seed 2191306982\n",
      "\tModel 16: seed 2244127738\n",
      "\tModel 17: seed 163126972\n",
      "\tModel 18: seed 2099464708\n",
      "\tModel 19: seed 987279723\n",
      "\tModel 20: seed 619768940\n",
      "\tModel 21: seed 657307788\n",
      "\tModel 22: seed 434656\n",
      "\tModel 23: seed 2446727812\n",
      "\tModel 24: seed 1293536377\n",
      "\tModel 25: seed 1917776284\n",
      "\tModel 26: seed 2970530657\n",
      "\tModel 27: seed 2377126869\n",
      "\tModel 28: seed 3486030984\n",
      "\tModel 29: seed 3518368696\n",
      "\n",
      "Topics ranked by stability:\n",
      "\n",
      "topic stability:\t0.406524\n",
      "\t\tterm                      frequency\n",
      "\t\tM136~I                        0.923\n",
      "\t\tM066                          0.923\n",
      "\t\tM131~E                        0.923\n",
      "\t\tM136~W                        0.923\n",
      "\t\tM157                          0.769\n",
      "\t\tM206~D                        0.692\n",
      "\n",
      "topic stability:\t0.390384\n",
      "\t\tterm                      frequency\n",
      "\t\tM136~W                        0.941\n",
      "\t\tM136~I                        0.882\n",
      "\t\tM131~E                        0.824\n",
      "\t\tM066                          0.765\n",
      "\t\tM157                          0.706\n",
      "\t\tM388                          0.647\n",
      "\t\tM066_M352~O                   0.588\n",
      "\t\tM206~D                        0.588\n",
      "\t\tM110                          0.471\n",
      "\t\tM096~D                        0.412\n",
      "\n",
      "topic stability:\t0.384850\n",
      "\t\tterm                      frequency\n",
      "\t\tM136~I                        1.000\n",
      "\t\tM131~E                        1.000\n",
      "\t\tM136~W                        1.000\n",
      "\n",
      "topic stability:\t0.337663\n",
      "\t\tterm                      frequency\n",
      "\t\tM136~W                        1.000\n",
      "\t\tM131~E                        1.000\n",
      "\t\tM136~I                        0.667\n",
      "\t\tM066                          0.333\n",
      "\n",
      "topic stability:\t0.337451\n",
      "\t\tterm                      frequency\n",
      "\t\tM136~I                        0.933\n",
      "\t\tM131~E                        0.933\n",
      "\t\tM136~W                        0.867\n",
      "\n",
      "topic stability:\t0.334537\n",
      "\t\tterm                      frequency\n",
      "\t\tM131~E                        0.857\n",
      "\n",
      "topic stability:\t0.329099\n",
      "\t\tterm                      frequency\n",
      "\t\tM136~W                        0.917\n",
      "\t\tM131~E                        0.917\n",
      "\t\tM136~I                        0.833\n",
      "\t\tM157                          0.583\n",
      "\t\tM206~D                        0.500\n",
      "\t\tM066                          0.500\n",
      "\n",
      "topic stability:\t0.324276\n",
      "\t\tterm                      frequency\n",
      "\t\tM131~E                        0.938\n",
      "\t\tM136~W                        0.875\n",
      "\n",
      "topic stability:\t0.320872\n",
      "\t\tterm                      frequency\n",
      "\t\tM131~E                        0.863\n",
      "\t\tM136~W                        0.804\n",
      "\t\tM136~I                        0.745\n",
      "\t\tM157                          0.627\n",
      "\t\tM066                          0.627\n",
      "\n",
      "topic stability:\t0.319295\n",
      "\t\tterm                      frequency\n",
      "\t\tM131~E                        0.889\n",
      "\t\tM136~I                        0.889\n",
      "\t\tM136~W                        0.852\n",
      "\t\tM066                          0.741\n",
      "\t\tM157                          0.593\n",
      "\t\tM096~D                        0.370\n",
      "\t\tM388                          0.333\n",
      "\t\tM259                          0.148\n",
      "\t\tM263                          0.111\n",
      "\t\tM329                          0.074\n",
      "\n",
      "topic stability:\t0.314547\n",
      "\t\tterm                      frequency\n",
      "\t\tM136~W                        0.853\n",
      "\t\tM066                          0.618\n",
      "\t\tM157                          0.500\n",
      "\t\tM206~D                        0.441\n",
      "\t\tM066_M352~O                   0.324\n",
      "\t\tM259                          0.206\n",
      "\t\tM329                          0.118\n",
      "\t\tM347                          0.088\n",
      "\t\tM151                          0.059\n",
      "\t\tM201                          0.029\n",
      "\n",
      "topic stability:\t0.313737\n",
      "\t\tterm                      frequency\n",
      "\t\tM131~E                        0.900\n",
      "\t\tM136~W                        0.850\n",
      "\t\tM136~I                        0.850\n",
      "\n",
      "topic stability:\t0.302023\n",
      "\t\tterm                      frequency\n",
      "\t\tM136~W                        1.000\n",
      "\t\tM131~E                        0.875\n",
      "\t\tM136~I                        0.750\n",
      "\n",
      "topic stability:\t0.291407\n",
      "\t\tterm                      frequency\n",
      "\t\tM131~E                        0.857\n",
      "\t\tM136~W                        0.828\n",
      "\t\tM136~I                        0.700\n",
      "\t\tM066                          0.581\n",
      "\t\tM157                          0.478\n",
      "\n",
      "topic stability:\t0.291297\n",
      "\t\tterm                      frequency\n",
      "\t\tM136~W                        0.833\n",
      "\t\tM131~E                        0.792\n",
      "\n",
      "topic stability:\t0.287739\n",
      "\t\tterm                      frequency\n",
      "\t\tM131~E                        0.900\n",
      "\t\tM136~W                        0.825\n",
      "\t\tM136~I                        0.675\n",
      "\t\tM066                          0.650\n",
      "\t\tM157                          0.375\n",
      "\n",
      "topic stability:\t0.286112\n",
      "\t\tterm                      frequency\n",
      "\t\tM131~E                        0.957\n",
      "\t\tM136~W                        0.783\n",
      "\t\tM136~I                        0.739\n",
      "\t\tM157                          0.522\n",
      "\t\tM066                          0.478\n",
      "\t\tM206~D                        0.435\n",
      "\n",
      "topic stability:\t0.285804\n",
      "\t\tterm                      frequency\n",
      "\t\tM131~E                        0.880\n",
      "\t\tM136~W                        0.840\n",
      "\t\tM136~I                        0.680\n",
      "\t\tM157                          0.600\n",
      "\t\tM066                          0.520\n",
      "\t\tM206~D                        0.280\n",
      "\t\tM388                          0.240\n",
      "\t\tM110                          0.200\n",
      "\t\tM096~D                        0.200\n",
      "\t\tM066_M352~O                   0.200\n",
      "\n",
      "topic stability:\t0.282415\n",
      "\t\tterm                      frequency\n",
      "\t\tM131~E                        0.823\n",
      "\t\tM136~W                        0.772\n",
      "\t\tM136~I                        0.722\n",
      "\t\tM066                          0.595\n",
      "\t\tM157                          0.544\n",
      "\t\tM206~D                        0.456\n",
      "\t\tM388                          0.329\n",
      "\n",
      "topic stability:\t0.278470\n",
      "\t\tterm                      frequency\n",
      "\t\tM136~W                        0.750\n",
      "\t\tM136~I                        0.667\n",
      "\t\tM388                          0.667\n",
      "\t\tM157                          0.583\n",
      "\t\tM206~D                        0.500\n",
      "\t\tM110                          0.500\n",
      "\t\tM066                          0.417\n",
      "\t\tM218                          0.250\n",
      "\t\tM347                          0.250\n",
      "\t\tM370                          0.167\n",
      "\n",
      "topic stability:\t0.269773\n",
      "\t\tterm                      frequency\n",
      "\t\tM131~E                        0.773\n",
      "\t\tM066                          0.727\n",
      "\t\tM136~W                        0.727\n",
      "\t\tM136~I                        0.727\n",
      "\t\tM157                          0.591\n",
      "\t\tM206~D                        0.500\n",
      "\t\tM388                          0.364\n",
      "\n",
      "topic stability:\t0.267561\n",
      "\t\tterm                      frequency\n",
      "\t\tM131~E                        0.867\n",
      "\t\tM136~W                        0.733\n",
      "\t\tM136~I                        0.600\n",
      "\t\tM066                          0.600\n",
      "\t\tM157                          0.600\n",
      "\t\tM206~D                        0.567\n",
      "\t\tM388                          0.533\n",
      "\n",
      "topic stability:\t0.266535\n",
      "\t\tterm                      frequency\n",
      "\t\tM136~W                        0.824\n",
      "\t\tM131~E                        0.804\n",
      "\n",
      "topic stability:\t0.262443\n",
      "\t\tterm                      frequency\n",
      "\t\tM131~E                        0.841\n",
      "\t\tM136~W                        0.795\n",
      "\n",
      "topic stability:\t0.253229\n",
      "\t\tterm                      frequency\n",
      "\t\tM131~E                        0.846\n",
      "\n",
      "topic stability:\t0.251379\n",
      "\t\tterm                      frequency\n",
      "\t\tM131~E                        0.941\n",
      "\n",
      "topic stability:\t0.248417\n",
      "\t\tterm                      frequency\n",
      "\t\tM131~E                        0.762\n",
      "\t\tM136~W                        0.762\n",
      "\t\tM136~I                        0.714\n",
      "\t\tM066                          0.476\n",
      "\t\tM206~D                        0.476\n",
      "\t\tM388                          0.429\n",
      "\t\tM157                          0.429\n",
      "\t\tM066_M352~O                   0.381\n",
      "\t\tM096~D                        0.238\n",
      "\n",
      "topic stability:\t0.243299\n",
      "\t\tterm                      frequency\n",
      "\t\tM131~E                        0.793\n",
      "\t\tM136~W                        0.690\n",
      "\t\tM136~I                        0.552\n",
      "\n",
      "topic stability:\t0.056761\n",
      "\t\tterm                      frequency\n",
      "\t\tM260~1                        0.333\n",
      "\t\tM242~B_M096                   0.333\n",
      "\t\tM006~B@G                      0.333\n",
      "\t\t|M218+M099|                   0.333\n",
      "\t\tM124                          0.333\n",
      "\t\tM329                          0.333\n",
      "\t\tM387                          0.333\n",
      "\t\tM248~C                        0.333\n",
      "\t\tM059                          0.333\n",
      "\t\tM346~D                        0.333\n"
     ]
    }
   ],
   "source": [
    "# Relearn an LDA model n_runs times:\n",
    "n_runs = 30\n",
    "\n",
    "# LDA model parameters:\n",
    "num_topics = 30\n",
    "chunksize = 500\n",
    "passes = 50\n",
    "iterations = 5000\n",
    "eval_every = 1\n",
    "\n",
    "import random\n",
    "# kmedoids implementation due to Bauckhage, C. Numpy/scipy Recipes for Data\n",
    "# Science: k-Medoids Clustering[R]. Technical Report, University of Bonn, 2015.\n",
    "# https://github.com/letiantian/kmedoids\n",
    "import kmedoids\n",
    "\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter('ignore')\n",
    "\n",
    "    topic_term_matrices = None\n",
    "    print(\"Training %d LDA models...\"%(n_runs))\n",
    "    for run in range(n_runs):\n",
    "        seed=random.randint(0,0xffffffff)\n",
    "        print(\"\\tModel %d: seed %d\"%(run, seed))\n",
    "\n",
    "        stability_model=LdaModel(\n",
    "            corpus=corpus, \\\n",
    "            id2word=dictionary, \\\n",
    "            num_topics=num_topics, \\\n",
    "            chunksize=chunksize, \\\n",
    "            passes=passes, \\\n",
    "            iterations=iterations, \\\n",
    "            eval_every=eval_every, \\\n",
    "            random_state=seed\n",
    "        )\n",
    "        \n",
    "        if topic_term_matrices is None:\n",
    "            topic_term_matrices = stability_model.get_topics()\n",
    "        else:\n",
    "            topic_term_matrices = np.concatenate( [topic_term_matrices, stability_model.get_topics()] )\n",
    "    \n",
    "    from sklearn.cluster import KMeans\n",
    "    from sklearn.decomposition import PCA\n",
    "    from sklearn import metrics\n",
    "    from sklearn.preprocessing import normalize as sknormalize\n",
    "    \n",
    "    n_clusters=num_topics\n",
    "\n",
    "    lda_stability_data=topic_term_matrices\n",
    "    from sklearn.metrics.pairwise import pairwise_distances\n",
    "    D = pairwise_distances(lda_stability_data, metric='euclidean')\n",
    "    M, C = kmedoids.kMedoids(D, n_clusters)\n",
    "    \n",
    "def RBO(T1, T2, p, d):\n",
    "    T1 = T1[:d]\n",
    "    T2 = T2[:d]\n",
    "    Xd = len(set(T1).intersection(set(T2)))\n",
    "    return (Xd/d)*(p**d) + ((1-p)/p)*sum( [(len(set(T1[:i]).intersection(T2[:i]))/i) * (p**i) for i in range(1,d+1)] )\n",
    "\n",
    "def var(term, ts, d):\n",
    "    ts=[ t[:d] for t in ts ]\n",
    "    indices = [ t.index(term) for t in ts if term in t ]\n",
    "    mu = sum(indices)/len(indices)\n",
    "    v = 0\n",
    "    for i in range(d):\n",
    "        p_i = len([t for t in ts if t[i] == term])\n",
    "        v += p_i*((i - mu)**2)\n",
    "    return mu,v**0.5\n",
    "\n",
    "clusters = []\n",
    "d=10\n",
    "# Compute pairwise RBO within each cluster\n",
    "# Print mean RBO per cluster\n",
    "for label in C:\n",
    "    stability = []\n",
    "    counts = defaultdict(int)\n",
    "    for i in range(len(C[label])):\n",
    "        point_idx = C[label][i]\n",
    "        terms = np.argsort(data[point_idx])[::-1]\n",
    "        for term in terms[:d]:\n",
    "            counts[term]+=1\n",
    "        for j in range(len(C[label])):\n",
    "            if j<=i:\n",
    "                continue\n",
    "            point_idx_2 = C[label][j]\n",
    "            terms_2 = np.argsort(data[point_idx_2])[::-1]\n",
    "            stability.append( RBO(terms, terms_2, 0.9, d) )\n",
    "    if len(stability) == 0:\n",
    "        continue\n",
    "    counts=({ id2word[t]:counts[t]/len(C[label]) for t in counts })\n",
    "    stability = sum(stability)/len(stability)\n",
    "    terms = np.argsort(data[M[label]])[::-1]\n",
    "    clusters += [ ([id2word[term] for term in terms[:d]], stability, counts) ]\n",
    "\n",
    "print(\"\\nTopics ranked by stability:\")\n",
    "clusters.sort(key=lambda x:-x[1])\n",
    "for c in clusters:\n",
    "    print()\n",
    "    print(\"topic stability:\\t%f\"%(c[1],))\n",
    "    print(\"\\t\\t{0:<20}{1:>15}\".format(\"term\",\"frequency\"))\n",
    "    term_stability = [(term,c[2][term]) for term in c[0] ]\n",
    "    term_stability.sort(key=lambda x:-x[1])\n",
    "    print(\"\\t\\t{0:<20}{1:>15.3f}\".format(term_stability[0][0],term_stability[0][1]))\n",
    "    for t,c in term_stability[1:]:\n",
    "        print(\"\\t\\t{0:<20}{1:>15.3f}\".format(t,c))\n"
   ]
  },
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   "metadata": {},
   "source": [
    "## 7. Conclusion\n",
    "\n",
    "Beyond reproducing our published results, we hope this notebook serves as a means for readers to experiment with our code and see what different models are capable of learning about proto-Elamite. As demonstrated in the introductory sections, the `pype` package included with this notebook provides an off-the-shelf interface for working with the proto-Elamite corpus, a copy of which is included in `data/cdli_atf_20180602.txt.bz2`. \n",
    "\n",
    "If you use this code in your research, we ask that you cite\n",
    "\n",
    "> Born, Logan, Kate Kelley, Nishant Kambhatla, Carolyn Chen, and Anoop Sarkar. 2019. Sign clustering and topic extraction in proto-Elamite. In *Proceedings of the Third Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL-2019)*. Minneapolis, Minnesota.\n",
    "\n",
    "A link to the paper will be added closer to the date of the workshop.\n",
    "\n",
    "\n",
    "## 7.1 Acknowledgments\n",
    "\n",
    "We wish to thank the [CDLI](https://cdli.ucla.edu) for their efforts producing and publishing the transliterations used in this work. \n",
    "\n",
    "Our implementation of [Mäntylä et al. (2018)](https://arxiv.org/pdf/1808.08098.pdf) uses the [$k$-medoids implementation](https://github.com/letiantian/kmedoids) due to [Bauckhage 2015](https://www.researchgate.net/publication/272351873_NumPy_SciPy_Recipes_for_Data_Science_k-Medoids_Clustering).\n",
    "\n",
    "## 7.2. Additional Resources\n",
    "\n",
    "The [CDLI wiki](http://cdli.ox.ac.uk/wiki/doku.php?id=proto-elamite) provides a summary of the proto-Elamite script and an extensive bibliography for further reading. \n",
    "\n",
    "Questions and feedback can be addressed to loborn@sfu.ca."
   ]
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