notebooks/cdh-ch5-text-alignment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Text Alignment: A Method for the Warping of Semantic Space \n",
    "\n",
    "by James E. Dobson (James.E.Dobson@Dartmouth.edu)\n",
    "___\n",
    "\n",
    "*Note:* This is one in [a series of documents and notebooks](https://jeddobson.github.io/textmining-docsouth/) that will document and evaluate various machine learning and text mining tools for use in literary studies. These notebooks form the practical and critical archive of my new book-in-progress, _Digital Humanities and the Search for a Method_. I have published a critique of some existing methods (Dobson 2015) that takes up some of these concerns and provides some theoretical background for my account of computational methods as used within the humanities.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Revision Date and Notes:\n",
    "* 01/18/2018: Initial version (james.e.dobson@dartmouth.edu)\n",
    "* 01/27/2018: Updated with references\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the past several years the use of word embedding has become (for computational humanists) an increasingly popular activity for mapping possible semantic relations in large datasets and text archives. Trained word vector datasets produced using the word2vec model can map both positive and negative of relations between supplied words. The authors of the word2vec model have provided a set of mappings trained on a Google generated textual archive. This is a contemporary archive and while it has the potential for some use by humanists, the relations extracted from the provided model are not suitable for text mining of historical texts. The semantic space modeled by the word2vec using (what are assumed mostly) contemporary sources has limited use for understanding either a) semantic shift or b) historical relations (Hamilton 2016). \n",
    "\n",
    "A method of \"aligning\" or warping multiple semantic spaces is needed in order to extract potentially more meaningful relations between a text, a set of texts, or the larger historical semantic context in which these texts were constructed and circulated. The term \"alignment\" is useful in this context because it names the need for producing a deformation of one vector space to another. While individual texts (at the codex or volume level) typically do not provide enough textual data for extracting semantic relations with the word2vec model, collections of texts can be modeled and compared. These collections could be based on author, gender, race, genre, period, etc. These collections of texts can then be modeled via word2vec and aligned to the semantic space generated by iterations of word2vec on other collections.\n",
    "\n",
    "Aligning the semantic space of one of collection of texts to another enables a greater granulatity of control over the composition of the collective semantic space. Aligning literary to non-literary spaces might present opportunities for examining the degree to which these texts participate in or are outliers of the more general semantic space of their historical moment. Historical relations can be transformed from one moment to another. Alignment might also make it possible to identify creative variations on commonplace relations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import gensim\n",
    "import collections\n",
    "import numpy as np\n",
    "import scipy.spatial.distance as sd\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "google_model = gensim.models.Word2Vec.load_word2vec_format('~/Downloads/GoogleNews-vectors-negative300.bin'\n",
    "                                                           ,binary=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# this is a model of the complete DocSouth North American Slave Narrative archive\n",
    "src_model = gensim.models.Word2Vec.load_word2vec_format('na-slave-narratives.bin',\n",
    "                                                        binary=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# first extract shared vocabulary\n",
    "common_vocab = [word for word in google_model.vocab if word in src_model.vocab]\n",
    "common_vectors_google = google_model[common_vocab]\n",
    "common_vectors_src = src_model[common_vocab]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 26304 common words\n"
     ]
    }
   ],
   "source": [
    "print(\"Found\",len(common_vocab),\"common words\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now use Sklearn's LinearRegression to combine vector space\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lin_model = LinearRegression()\n",
    "lin_model.fit(common_vectors_google, common_vectors_src)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "shared_vectors = collections.OrderedDict()\n",
    "for word in google_model.vocab:\n",
    "    word_vector = lin_model.predict(google_model[word].reshape(1, -1))\n",
    "    shared_vectors[word] = word_vector.reshape(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# now add words only in src_model\n",
    "for word in [word for word in src_model.vocab if word not in common_vocab]:\n",
    "    shared_vectors[word] = src_model[word]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vocab = list(shared_vectors.keys())\n",
    "shared_embeddings = np.array(list(shared_vectors.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_most_similar_word2vec(word,model,topn=10):\n",
    "    neighbors = model.most_similar(word,topn=topn)\n",
    "    \n",
    "    neighbor_list=list()\n",
    "    words=list()\n",
    "    for i in neighbors:\n",
    "        words.append(i[0])\n",
    "        neighbor_list.append(model.wv[i[0]])\n",
    "\n",
    "    pca = PCA(n_components=2)\n",
    "    plot_data = pca.fit_transform(neighbor_list)\n",
    "\n",
    "    xs = plot_data[:, 0]\n",
    "    ys = plot_data[:, 1]\n",
    "    fig = plt.figure(figsize=(20, 15))\n",
    "\n",
    "    plt.clf()\n",
    "    plt.scatter(xs, ys, marker = 'o')\n",
    "    \n",
    "    for i, w in enumerate(words):\n",
    "        plt.annotate(w, xy = (xs[i], ys[i]), xytext = (3, 3),\n",
    "          textcoords = 'offset points', ha = 'left', va = 'top')\n",
    "            \n",
    "    plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def combined_most_similar_neighbors(word, topn=20):\n",
    "    neighbor_list=list()\n",
    "    ind = vocab.index(word)\n",
    "    encoding = shared_embeddings[ind]\n",
    "    scores = sd.cdist([encoding], shared_embeddings, \"cosine\")[0]\n",
    "    sorted_ids = np.argsort(scores)\n",
    "    for i in range(1, topn + 1):\n",
    "        neighbor_list.append((vocab[sorted_ids[i]], scores[sorted_ids[i]]))\n",
    "    return(neighbor_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot_most_similar_neighbors(word,topn=10):\n",
    "    neighbor_list=list()\n",
    "    ind = vocab.index(word)\n",
    "    encoding = shared_embeddings[ind]\n",
    "    scores = sd.cdist([encoding], shared_embeddings, \"cosine\")[0]\n",
    "    sorted_ids = np.argsort(scores)\n",
    "    words=list()\n",
    "    for i in range(1, topn + 1):\n",
    "        words.append(vocab[sorted_ids[i]])\n",
    "        index = vocab.index(vocab[sorted_ids[i]])\n",
    "        neighbor_list.append(shared_embeddings[index])\n",
    "    \n",
    "    pca = PCA(n_components=2)\n",
    "    plot_data = pca.fit_transform(neighbor_list)\n",
    "\n",
    "    xs = plot_data[:, 0]\n",
    "    ys = plot_data[:, 1]\n",
    "    fig = plt.figure(figsize=(20, 15))\n",
    "    plt.clf()\n",
    "    plt.scatter(xs, ys, marker = 'o')\n",
    "    \n",
    "    for i, w in enumerate(words):\n",
    "        plt.annotate(w, xy = (xs[i], ys[i]), xytext = (3, 3),\n",
    "          textcoords = 'offset points', ha = 'left', va = 'top')\n",
    "            \n",
    "    plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('lucretia_mott', 0.6808589696884155),\n",
       " ('gage', 0.604787290096283),\n",
       " ('miss_susan_b', 0.6004625558853149),\n",
       " ('john_g_whittier', 0.5983288884162903),\n",
       " ('broadway_tabernacle', 0.5822384357452393),\n",
       " ('folsom', 0.5798661708831787),\n",
       " ('boston_post', 0.5795968770980835),\n",
       " ('garrison_phillips', 0.5787340402603149),\n",
       " ('h_garnet', 0.5783672332763672),\n",
       " ('alcott', 0.572452187538147)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "src_model.most_similar(positive=['nat_turner', 'woman_suffrage'], negative=['insurrection'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('mr_douglass', 0.6789201498031616),\n",
       " ('wendell_phillips', 0.6422789692878723),\n",
       " ('william_lloyd_garrison', 0.6040972471237183),\n",
       " ('autobiographies', 0.5962321162223816),\n",
       " ('douglass', 0.589881420135498),\n",
       " ('decoration_day', 0.5729281306266785),\n",
       " ('mr_garrison', 0.5727505683898926),\n",
       " (\"douglass's\", 0.5660401582717896),\n",
       " ('anti_slavery_convention', 0.563313364982605),\n",
       " (\"mr_douglass's\", 0.5495040416717529),\n",
       " ('anti_slavery_movement', 0.5458614826202393),\n",
       " ('agitator', 0.5411466360092163),\n",
       " ('various_phases', 0.5406709909439087),\n",
       " ('patrick_henry', 0.5403376817703247),\n",
       " ('harriet_beecher_stowe', 0.5360968708992004),\n",
       " ('gerritt_smith', 0.5353233814239502),\n",
       " ('henry_wilson', 0.534428596496582),\n",
       " ('charles_l_remond', 0.5323055386543274),\n",
       " ('journalistic', 0.5313712954521179),\n",
       " ('autographs', 0.5309398174285889)]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "src_model.most_similar(\"frederick_douglass\",topn=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Fredrick_Douglass', 0.6643674969673157),\n",
       " ('Harriet_Tubman', 0.6538548469543457),\n",
       " ('Langston_Hughes', 0.6378294825553894),\n",
       " ('WEB_Dubois', 0.6361298561096191),\n",
       " ('abolitionist_Frederick_Douglass', 0.6212432384490967),\n",
       " ('WEB_DuBois', 0.6167810559272766),\n",
       " ('Sojourner_Truth', 0.6166481971740723),\n",
       " ('Booker_T._Washington', 0.5926769971847534),\n",
       " ('Phillis_Wheatley', 0.5811346769332886),\n",
       " ('Thurgood_Marshall', 0.5717906355857849),\n",
       " ('G._Woodson', 0.568604588508606),\n",
       " ('WEB_Du_Bois', 0.5670568943023682),\n",
       " ('Ralph_Bunche', 0.5626702904701233),\n",
       " ('Phyllis_Wheatley', 0.5608549118041992),\n",
       " ('Mary_McLeod_Bethune', 0.5546070337295532),\n",
       " ('Fannie_Lou_Hamer', 0.5512949228286743),\n",
       " ('Tubman', 0.5479763746261597),\n",
       " ('Benjamin_Banneker', 0.5479563474655151),\n",
       " ('Eleanor_Roosevelt', 0.5426533222198486),\n",
       " ('Malcolm_X', 0.5422308444976807)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "google_model.most_similar(\"Frederick_Douglass\",topn=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('mr_douglass', 0.32107986263292665),\n",
       " ('wendell_phillips', 0.35772104992424181),\n",
       " ('william_lloyd_garrison', 0.39590273197090209),\n",
       " ('BookExpo', 0.39807692986095866),\n",
       " ('Andrei_Sakharov', 0.40841162322395386),\n",
       " ('douglass', 0.41011861214084999),\n",
       " ('feminist_icon', 0.41086614525319021),\n",
       " ('Julian_Bond', 0.42012511674663988),\n",
       " ('Paul_Robeson', 0.42399577753377249),\n",
       " ('Steinem', 0.42421127121989977),\n",
       " ('decoration_day', 0.42707189705680892),\n",
       " ('mr_garrison', 0.42724955171080203),\n",
       " ('Louise_Mirrer', 0.42754550148413384),\n",
       " ('historian_Doris_Kearns', 0.42768691113746837),\n",
       " ('Wally_Serote', 0.43090307555114549),\n",
       " ('Mahashweta_Devi', 0.43156523792348467),\n",
       " ('Tony_Judt', 0.43233772692871941),\n",
       " (\"douglass's\", 0.43395979808404406),\n",
       " ('Harriet_Tubman', 0.43432488585549678),\n",
       " ('Eugene_V._Debs', 0.43469860966633733)]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_most_similar_neighbors(\"frederick_douglass\",topn=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FiUMAAAAAhYlDAAAAAIWJQwAAAACFiUMAAAAAhYlDAAAAAIWJQwAAAACFiUMA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x269aa1240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_most_similar_word2vec(\"abolitionism\",src_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cgFpg8ii4GejFEs3M6a3MJpJGx9SaYgYf9cGZydG0zYTIDCk55WQR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\n165dm4+tXyy7rq4u++67b37wgx9s83uZPHlyRo0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      "text/plain": [
       "<matplotlib.figure.Figure at 0x38e481dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_most_similar_word2vec(\"abolitionism\",google_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x38e477b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_most_similar_neighbors(\"abolitionism\",topn=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References\n",
    "\n",
    "Hamilton, William L., Jure Leskovec, and Dan Jurafsky. (2016) “Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change,” ArXiv Preprint ArXiv:1605.09096, https://arxiv.org/abs/1605.09096."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}