SyntaxNet and NLTK examples.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#USING THIS TUTORIAL\n",
    "#  https://github.com/krikit/syntaxnet/blob/master/syntaxnet.ipynb\n",
    "import nltk\n",
    "from IPython.display import Image, display\n",
    "from nltk import Tree\n",
    "from nltk.draw.util import CanvasFrame\n",
    "from nltk.draw import TreeWidget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from nltk.parse import stanford"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'data/out_defs.txt'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-70b6dc465e1e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'data/out_defs.txt'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mall_data_texts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'data/out_defs.txt'"
     ]
    }
   ],
   "source": [
    "with open('data/out_defs.txt','r') as f1:\n",
    "    all_data_texts = f1.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== SENTENCES ==========\n",
      "For every n let G_n be a group of size n.\n",
      "========== TOKENIZED ==========\n",
      "For every n let G_n be a group of size n .\n"
     ]
    }
   ],
   "source": [
    "#raw = all_data_texts[105]\n",
    "raw = 'For every n let G_n be a group of size n.'\n",
    "sent_detector = nltk.data.load('tokenizers/punkt/english.pickle')\n",
    "sents = sent_detector.tokenize(raw)\n",
    "\n",
    "\n",
    "print('========== SENTENCES ==========')\n",
    "print('\\n'.join(sents))\n",
    "\n",
    "print('========== TOKENIZED ==========')\n",
    "tokenized_sents = []\n",
    "for sent in sents:\n",
    "    words = nltk.word_tokenize(sent)\n",
    "    tokenized_sents.append(words)\n",
    "    print(' '.join(words))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For/IN every/DT n/JJ let/NN G_n/NNP be/VB a/DT group/NN of/IN size/NN n/NNS ./.\n"
     ]
    }
   ],
   "source": [
    "tagged_sents = []\n",
    "for tokens in tokenized_sents:\n",
    "    tagged_pairs = nltk.pos_tag(tokens)\n",
    "    tagged_sents.append(tagged_pairs)\n",
    "    print(' '.join(['%s/%s' % (word, tag) for word, tag in tagged_pairs]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SLF4J: Failed to load class \"org.slf4j.impl.StaticLoggerBinder\".\n",
      "SLF4J: Defaulting to no-operation (NOP) logger implementation\n",
      "SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.\n",
      "java.io.IOException: Unable to open \"/home/luis/Paquetes/stanford-parser-full-2018-02-27/models/edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz\" as class path, filename or URL\n",
      "\tat edu.stanford.nlp.io.IOUtils.getInputStreamFromURLOrClasspathOrFileSystem(IOUtils.java:480)\n",
      "\tat edu.stanford.nlp.io.IOUtils.readerFromString(IOUtils.java:617)\n",
      "\tat edu.stanford.nlp.parser.lexparser.LexicalizedParser.getParserFromTextFile(LexicalizedParser.java:541)\n",
      "\tat edu.stanford.nlp.parser.lexparser.LexicalizedParser.getParserFromFile(LexicalizedParser.java:406)\n",
      "\tat edu.stanford.nlp.parser.lexparser.LexicalizedParser.loadModel(LexicalizedParser.java:186)\n",
      "\tat edu.stanford.nlp.parser.lexparser.LexicalizedParser.main(LexicalizedParser.java:1400)\n",
      "Exception in thread \"main\" java.lang.NullPointerException\n",
      "\tat edu.stanford.nlp.parser.lexparser.LexicalizedParser.loadModel(LexicalizedParser.java:188)\n",
      "\tat edu.stanford.nlp.parser.lexparser.LexicalizedParser.main(LexicalizedParser.java:1400)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-8-0bf7415457f7>:7: DeprecationWarning: The StanfordParser will be deprecated\n",
      "Please use \u001b[91mnltk.parse.corenlp.CoreNLPParser\u001b[0m instead.\n",
      "  parser = stanford.StanfordParser(model_path='%s/models/edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz' % STANFORD_DIR)\n"
     ]
    },
    {
     "ename": "OSError",
     "evalue": "Java command failed : ['/usr/bin/java', '-mx4g', '-cp', '/home/luis/Paquetes/stanford-parser-full-2018-02-27/stanford-parser-3.9.1-models.jar:/home/luis/Paquetes/stanford-parser-full-2018-02-27/stanford-parser.jar:/home/luis/Paquetes/stanford-parser-full-2018-02-27/ejml-0.23.jar:/home/luis/Paquetes/stanford-parser-full-2018-02-27/slf4j-api.jar:/home/luis/Paquetes/stanford-parser-full-2018-02-27/stanford-parser-3.9.1-models.jar:/home/luis/Paquetes/stanford-parser-full-2018-02-27/slf4j-api-1.7.12-sources.jar:/home/luis/Paquetes/stanford-parser-full-2018-02-27/stanford-parser-3.9.1-sources.jar:/home/luis/Paquetes/stanford-parser-full-2018-02-27/stanford-parser-3.9.1-javadoc.jar', 'edu.stanford.nlp.parser.lexparser.LexicalizedParser', '-model', '/home/luis/Paquetes/stanford-parser-full-2018-02-27/models/edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz', '-sentences', 'newline', '-outputFormat', 'penn', '-tokenized', '-tagSeparator', '/', '-tokenizerFactory', 'edu.stanford.nlp.process.WhitespaceTokenizer', '-tokenizerMethod', 'newCoreLabelTokenizerFactory', '-encoding', 'utf8', '/tmp/tmpy0hs3ulh']",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-0bf7415457f7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstanford\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mStanfordParser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'%s/models/edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mSTANFORD_DIR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mparsed_sents\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mtree\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtagged_parse_sents\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtagged_sents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0mtree_sent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'\\n'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtree\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mparsed_sents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtree_sent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/nltk/parse/stanford.py\u001b[0m in \u001b[0;36mtagged_parse_sents\u001b[0;34m(self, sentences, verbose)\u001b[0m\n\u001b[1;32m    218\u001b[0m         \u001b[0;31m# We don't need to escape slashes as \"splitting is done on the last instance of the character in the token\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m         return self._parse_trees_output(\n\u001b[0;32m--> 220\u001b[0;31m             self._execute(\n\u001b[0m\u001b[1;32m    221\u001b[0m                 \u001b[0mcmd\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m                 \"\\n\".join(\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/nltk/parse/stanford.py\u001b[0m in \u001b[0;36m_execute\u001b[0;34m(self, cmd, input_, verbose)\u001b[0m\n\u001b[1;32m    259\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m                 \u001b[0mcmd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 261\u001b[0;31m                 stdout, stderr = java(\n\u001b[0m\u001b[1;32m    262\u001b[0m                     \u001b[0mcmd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclasspath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_classpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstdout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mPIPE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mPIPE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m                 )\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/nltk/internals.py\u001b[0m in \u001b[0;36mjava\u001b[0;34m(cmd, classpath, stdin, stdout, stderr, blocking)\u001b[0m\n\u001b[1;32m    138\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_decode_stdoutdata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstderr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Java command failed : \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcmd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: Java command failed : ['/usr/bin/java', '-mx4g', '-cp', '/home/luis/Paquetes/stanford-parser-full-2018-02-27/stanford-parser-3.9.1-models.jar:/home/luis/Paquetes/stanford-parser-full-2018-02-27/stanford-parser.jar:/home/luis/Paquetes/stanford-parser-full-2018-02-27/ejml-0.23.jar:/home/luis/Paquetes/stanford-parser-full-2018-02-27/slf4j-api.jar:/home/luis/Paquetes/stanford-parser-full-2018-02-27/stanford-parser-3.9.1-models.jar:/home/luis/Paquetes/stanford-parser-full-2018-02-27/slf4j-api-1.7.12-sources.jar:/home/luis/Paquetes/stanford-parser-full-2018-02-27/stanford-parser-3.9.1-sources.jar:/home/luis/Paquetes/stanford-parser-full-2018-02-27/stanford-parser-3.9.1-javadoc.jar', 'edu.stanford.nlp.parser.lexparser.LexicalizedParser', '-model', '/home/luis/Paquetes/stanford-parser-full-2018-02-27/models/edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz', '-sentences', 'newline', '-outputFormat', 'penn', '-tokenized', '-tagSeparator', '/', '-tokenizerFactory', 'edu.stanford.nlp.process.WhitespaceTokenizer', '-tokenizerMethod', 'newCoreLabelTokenizerFactory', '-encoding', 'utf8', '/tmp/tmpy0hs3ulh']"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from nltk.parse import stanford\n",
    "#STANFORD_DIR = '/home/luis/Paquetes/stanford-parser-full-2018-02-27' \n",
    "STANFORD_DIR = '/home/luis/Paquetes/stanford-parser-full-2018-02-27' \n",
    "os.environ['STANFORD_PARSER'] = '%s/stanford-parser.jar' % STANFORD_DIR\n",
    "os.environ['STANFORD_MODELS'] = '%s/stanford-parser-3.9.1-models.jar' % STANFORD_DIR\n",
    "parser = stanford.StanfordParser(model_path='%s/models/edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz' % STANFORD_DIR)\n",
    "parsed_sents = []\n",
    "for tree in parser.tagged_parse_sents(tagged_sents):\n",
    "    tree_sent = '\\n'.join([str(_) for _ in tree])\n",
    "    parsed_sents.append(tree_sent)\n",
    "    print(tree_sent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for idx, tree_str in enumerate(parsed_sents):\n",
    "    cf = CanvasFrame()\n",
    "    tree = Tree.fromstring(tree_str)\n",
    "    tc = TreeWidget(cf.canvas(), tree)\n",
    "    tc['node_font'] = 'arial 13 bold'\n",
    "    tc['leaf_font'] = 'arial 12'\n",
    "    tc['node_color'] = '#005990'\n",
    "    tc['leaf_color'] = '#175252'\n",
    "    tc['line_color'] = '#175252'\n",
    "    cf.add_widget(tc, 10, 10)\n",
    "    cf.print_to_file('tmp.ps')\n",
    "    cf.destroy()\n",
    "    os.system('convert tmp.ps tmp.png')    # need install: libpng, ghostscript, imagemagic\n",
    "    display(Image(filename='tmp.png'))\n",
    "    #os.remove('tmp.ps')\n",
    "    #os.remove('tmp.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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