All_About__NLP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# All About NLP : Basics\n",
    "\n",
    "This notebook demo's basic and introductory techniques in natural language processing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# One Hot Representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Time flies like an arrow', 'Fruit flies like a banana']"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus = [\n",
    "    'Time flies like an arrow',\n",
    "    'Fruit flies like a banana'\n",
    "]\n",
    "corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['time', 'flies', 'like', 'an', 'arrow', 'fruit', 'a', 'banana']"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_vocab(corpus):\n",
    "    vocab = []\n",
    "    for sentence in corpus:\n",
    "        for word in sentence.split():\n",
    "            if word not in vocab:\n",
    "                vocab.append(word.lower())\n",
    "    return vocab\n",
    "                \n",
    "vocab = get_vocab(corpus)\n",
    "vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 0, 1, 0, 1, 1],\n",
       "       [0, 0, 1, 1, 1, 1, 0]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "onehot_vectorizer = CountVectorizer(lowercase=True, binary=True)\n",
    "onehot = onehot_vectorizer.fit_transform(corpus).toarray()\n",
    "\n",
    "onehot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.figure(figsize=(15,5))\n",
    "sns.heatmap(onehot, annot=True, cbar=False, xticklabels=vocab, yticklabels=['Sentence 1','Sentence 2'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.49922133, 0.49922133, 0.        , 0.35520009, 0.        ,\n",
       "        0.35520009, 0.49922133],\n",
       "       [0.        , 0.        , 0.57615236, 0.40993715, 0.57615236,\n",
       "        0.40993715, 0.        ]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "tfidfvectorizer = TfidfVectorizer()\n",
    "tfidfvector = tfidfvectorizer.fit_transform(corpus).toarray()\n",
    "\n",
    "tfidfvector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(tfidfvector, annot=True, cbar=False, xticklabels=vocab, yticklabels=['sentence 1','sentence 2'] )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tokenization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy\n",
    "nlp = spacy.load('en')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"Mary, don't slap the green witch.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mary\n",
      ",\n",
      "do\n",
      "n't\n",
      "slap\n",
      "the\n",
      "green\n",
      "witch\n",
      ".\n"
     ]
    }
   ],
   "source": [
    "for tokens in nlp(text.lower()):\n",
    "    print(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import TweetTokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweet = u\"Snow White and the Seven Degrees #MakeAMovieCold @midnight:)\"\n",
    "tokenizer = TweetTokenizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Snow',\n",
       " 'White',\n",
       " 'and',\n",
       " 'the',\n",
       " 'Seven',\n",
       " 'Degrees',\n",
       " '#MakeAMovieCold',\n",
       " '@midnight',\n",
       " ':)']"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_tokens = tokenizer.tokenize(tweet)\n",
    "tweet_tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unigram, Bigram, Trigram, ... (N-Gram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "def n_gram(text, n):\n",
    "    return [ text[i:i+n] for i in range(len(text)-n+1) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Snow', 'White', 'and'],\n",
       " ['White', 'and', 'the'],\n",
       " ['and', 'the', 'Seven'],\n",
       " ['the', 'Seven', 'Degrees'],\n",
       " ['Seven', 'Degrees', '#MakeAMovieCold'],\n",
       " ['Degrees', '#MakeAMovieCold', '@midnight'],\n",
       " ['#MakeAMovieCold', '@midnight', ':)']]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_gram(tweet_tokens,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lemmas, Stems"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc = nlp(u\"he was running late\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "he \t --> -PRON-\n",
      "was \t --> be\n",
      "running \t --> run\n",
      "late \t --> late\n"
     ]
    }
   ],
   "source": [
    "for token in doc:\n",
    "    print(f'{token} \\t --> {token.lemma_}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# POS Tagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc = nlp(\"Mary slapped the green witch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mary \t PROPN\n",
      "slapped \t VERB\n",
      "the \t DET\n",
      "green \t ADJ\n",
      "witch \t NOUN\n"
     ]
    }
   ],
   "source": [
    "for token in doc:\n",
    "    print(f'{token} \\t {token.pos_}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chunking and NER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc = nlp(\"Mary slapped the green witch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mary : NP\n",
      "the green witch : NP\n"
     ]
    }
   ],
   "source": [
    "for chunk in doc.noun_chunks:\n",
    "    print(f'{chunk} : {chunk.label_}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from spacy import displacy\n",
    "\n",
    "doc = nlp(\"John was born in Alaska and studied in Cranberry Lemon University.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
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       "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    John\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
       "</mark>\n",
       " was born in \n",
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       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem\">GPE</span>\n",
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      ]
     },
     "metadata": {},
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   ],
   "source": [
    "displacy.render(doc, style='ent',jupyter=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Structure of the sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc = nlp(\"John is a good person\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
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       "    <path class=\"displacy-arrowhead\" d=\"M595,266.5 L587,254.5 603,254.5\" fill=\"currentColor\"/>\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "displacy.render(doc, style='dep', jupyter=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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