Chunking_spaCy.ipynb

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   "cell_type": "code",
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   "source": [
    "# spacy import convention\n",
    "import spacy\n",
    "\n",
    "# load the english model into nlp object.\n",
    "nlp=spacy.load('en_core_web_sm') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sample text\n",
    "text=\"\"\"Success isn't easy, and that's a good thing - at least in business. \n",
    "If it was easy, everybody would be doing it and your competition would be outrageous!\"\"\"\n",
    "\n",
    "# returns doc container with attributes \n",
    "doc=nlp(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loop through doc and return tokens with .text\n",
    "# token.root access the actual noun.\n",
    "tokenized=[(token.text,token.root) for token in doc.noun_chunks]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Success', Success),\n",
       " ('a good thing', thing),\n",
       " ('business', business),\n",
       " ('it', it),\n",
       " ('everybody', everybody),\n",
       " ('it', it),\n",
       " ('your competition', competition)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print result (noun phrases)\n",
    "tokenized"
   ]
  }
 ],
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