Spacy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spacy\n",
    "\n",
    "spaCy is an open-source software library for advanced Natural Language Processing, written in the programming languages Python and Cython. It is slightly more ready enterprise use cases. Its out of box POS tagger, NER analyzers are very popular. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"\"\"European authorities fined Google a record $5.1 billion on Wednesday for \n",
    "abusing its power in the mobile phone market and ordered the company to alter its practices\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('European', 'NORP'),\n",
       " ('Google', 'ORG'),\n",
       " ('$5.1 billion', 'MONEY'),\n",
       " ('Wednesday', 'DATE')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Install spacy\n",
    "$ pip install spacy\n",
    "\n",
    "Download en_core_web_sm module\n",
    "$ python -m spacy download en_core_web_sm\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "import spacy\n",
    "from spacy import displacy\n",
    "from collections import Counter\n",
    "import en_core_web_sm\n",
    "nlp = en_core_web_sm.load()\n",
    "\n",
    "doc = nlp(sentence)\n",
    "[(X.text, X.label_) for X in doc.ents]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display dependency graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "displacy.render(nlp(str(sentence)), style='dep', jupyter = True, options = {'distance': 120})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('European', 'ADJ', 'european'),\n",
       " ('authorities', 'NOUN', 'authority'),\n",
       " ('fined', 'VERB', 'fine'),\n",
       " ('Google', 'PROPN', 'Google'),\n",
       " ('record', 'NOUN', 'record'),\n",
       " ('$', 'SYM', '$'),\n",
       " ('5.1', 'NUM', '5.1'),\n",
       " ('billion', 'NUM', 'billion'),\n",
       " ('Wednesday', 'PROPN', 'Wednesday'),\n",
       " ('\\n', 'SPACE', '\\n'),\n",
       " ('abusing', 'VERB', 'abuse'),\n",
       " ('power', 'NOUN', 'power'),\n",
       " ('mobile', 'ADJ', 'mobile'),\n",
       " ('phone', 'NOUN', 'phone'),\n",
       " ('market', 'NOUN', 'market'),\n",
       " ('ordered', 'VERB', 'order'),\n",
       " ('company', 'NOUN', 'company'),\n",
       " ('alter', 'VERB', 'alter'),\n",
       " ('practices', 'NOUN', 'practice')]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(x.orth_,x.pos_, x.lemma_) \n",
    "     for x in [y for y in nlp(sentence) \n",
    "               if not y.is_stop and y.pos_ != 'PUNCT']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's see how to create text classifier using nltk and scikit learn."
   ]
  }
 ],
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