Wikipedia to Named Entity Recognition.ipynb

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   "cells": [
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     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext autoreload\n",
      "%autoreload 2\n",
      "from IPython.display import Image\n",
      "from graphviz import Digraph\n",
      "import wikipedia_ner.parse as wikipedia_ner"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ImportError",
       "evalue": "No module named 'xml_cleaner.wiki_markup_processing'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-1-e2e2a3ef45df>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgraphviz\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDigraph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mwikipedia_ner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mwikipedia_ner\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Users/jonathanraiman/Desktop/Coding/python_packages/wikipedia_ner/wikipedia_ner/parse/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \"\"\"\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mparse_dump\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mdump_result\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDumpResult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mpages\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mParsedPage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mParsedPageChild\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mParsedPageParent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/jonathanraiman/Desktop/Coding/python_packages/wikipedia_ner/wikipedia_ner/parse/parse.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mepub_conversion\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mconvert_wiki_to_lines\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mepub_conversion\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwiki_decoder\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0malmost_smart_open\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mline_converter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mdump_result\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDumpResult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.4/site-packages/epub_conversion/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \"\"\"\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mconverter\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mConverter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mwiki_decoder\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mconvert_wiki_to_lines\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert_wiki_to_corpus\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.4/site-packages/epub_conversion/converter.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_files_from_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert_epub_to_lines\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert_lines_to_text\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopen_book\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgzip\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mConverter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \t\"\"\"\n",
        "\u001b[0;32m/usr/local/lib/python3.4/site-packages/epub_conversion/utils.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mxml_cleaner\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mto_raw_text\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mepub\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mopen_epub\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBadEpubFile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mzipfile\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mBadZipFile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.4/site-packages/xml_cleaner/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mpyximport\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minstall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mwiki_markup_processing\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mto_raw_text\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mremove_brackets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mword_tokenizer\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msplit_sentences\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit_and_group_sentences\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit_punct\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mImportError\u001b[0m: No module named 'xml_cleaner.wiki_markup_processing'"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def update_graph(graph, dict_node, code = [], name = None):\n",
      "    if name == None and len(dict_node.keys()) > 1:\n",
      "        name = 'ROOT'\n",
      "    elif name == None and len(dict_node.keys()) == 1:\n",
      "        return update_graph(graph, dict_node[list(dict_node.keys())[0]], code, list(dict_node.keys())[0])\n",
      "    \n",
      "    graph.node(str(code), \"%s %r\" % (name, code))\n",
      "    # connect to parents\n",
      "    if len(code) > 0:\n",
      "        graph.edge(str(code[:-1]), str(code))\n",
      "    \n",
      "    for i, subkey in enumerate(dict_node.keys()):\n",
      "        update_graph(graph, dict_node[subkey], code + [i], subkey)\n",
      "    return graph"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Example tree structure visible in Wikipedia or in a taxonomy"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "graph = Digraph(comment='The Round Table', format='png')\n",
      "update_graph(graph, {\n",
      "    \"World\": {\n",
      "    \"News\": {\n",
      "        \"World news\": {},\n",
      "        \"Tech news\": {}\n",
      "    },\n",
      "    \"Science\" : {\n",
      "        \"solar\": {},\n",
      "        \"CS\":{}\n",
      "    },\n",
      "    \"Religion\" : {\n",
      "        \"Temples\":{},\n",
      "        \"Study\":{}\n",
      "    },\n",
      "    \"Politics\": {\n",
      "        \"USA Politics\": {},\n",
      "        \"French Politics\": {}\n",
      "    }\n",
      "    }})\n",
      "Image(graph.render('test-output/round-table', view=True))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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RwySKTps2zTp06JDmLcqM6iMCZUY/0goIQAACEIBANhBA8MmGXqaNEIAABCAA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\ngy+F4NHAuX///jGzYJKV1NVXX+0euGX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u86FDh4Z0X0ulkt/9V3WcNm1aqEOHDq7uakew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\nQsmh5TmtviAefeL6oKAzSYjT2FZC6e+//+4MYs4++2w3vkU8KYhceny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CNHjRoVhg8fHvBIR+lM7R/TB5DKde6559Y07YzBM800U3jssce0jkUj9/XfOgKOQE8EfHzuiUde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\n",
       "prompt_number": 3,
       "text": [
        "<IPython.core.display.Image at 0x1025e4b00>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here we extract wikipedia articles that are not special pages,\n",
      "redirection, or disambiguations.\n",
      "Among those pages we now take out strings of text with references to other articles\n",
      "to learn named entity recognition over a hierarchical softmax tree"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "parseresult = wikipedia_ner.parse_dump(\"/Users/jonathanraiman/Desktop/Coding/enwiki.bz2\",\n",
      "                            max_articles = 200)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "200 articles seen so far. Processing 35.408 articles / s : position 9851453\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "most_common_category = wikipedia_ner.ParsedPage.categories_counter.most_common(1)[0][0]\n",
      "print(\"In '%s' the children are %r\" % (most_common_category, \", \".join([parseresult.index2target[child] for child in list(wikipedia_ner.ParsedPage.categories[most_common_category].children)])))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "In 'Category : Member states of the United Nations' the children are 'Afghanistan, Algeria, Andorra, Antigua and Barbuda, Azerbaijan, Angola, Albania'\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}