2.8 N-grams.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.8 N-grams\n",
    "\n",
    "We can break our text down into n-grams to check our preprocessing, explore the contents of our data, or create new features for machine learning. An n-gram is simply a sequence of neighbouring n words (or tokens), where n can be any number. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['the', 'rise', 'of', 'artificial', 'intelligence', 'has', 'led', 'to', 'significant', 'advancements', 'in', 'natural', 'language', 'processing', 'computer', 'vision', 'and', 'other', 'fields', 'machine', 'learning', 'algorithms', 'are', 'becoming', 'more', 'sophisticated', 'enabling', 'computers', 'to', 'perform', 'complex', 'tasks', 'that', 'were', 'once', 'thought', 'to', 'be', 'the', 'exclusive', 'domain', 'of', 'humans', 'with', 'the', 'advent', 'of', 'deep', 'learning', 'neural', 'networks', 'have', 'become', 'even', 'more', 'powerful', 'capable', 'of', 'processing', 'vast', 'amounts', 'of', 'data', 'and', 'learning', 'from', 'it', 'in', 'ways', 'that', 'were', 'not', 'possible', 'before', 'as', 'a', 'result', 'ai', 'is', 'increasingly', 'being', 'used', 'in', 'a', 'wide', 'range', 'of', 'industries', 'from', 'healthcare', 'to', 'finance', 'to', 'transportation', 'and', 'its', 'impact', 'is', 'only', 'set', 'to', 'grow', 'in', 'the', 'years', 'to', 'come']\n"
     ]
    }
   ],
   "source": [
    "tokens = ['the', 'rise', 'of', 'artificial', 'intelligence', 'has', 'led', 'to', 'significant', 'advancements', 'in', 'natural', 'language', 'processing', 'computer', 'vision', 'and', 'other', 'fields', 'machine', 'learning', 'algorithms', 'are', 'becoming', 'more', 'sophisticated', 'enabling', 'computers', 'to', 'perform', 'complex', 'tasks', 'that', 'were', 'once', 'thought', 'to', 'be', 'the', 'exclusive', 'domain', 'of', 'humans', 'with', 'the', 'advent', 'of', 'deep', 'learning', 'neural', 'networks', 'have', 'become', 'even', 'more', 'powerful', 'capable', 'of', 'processing', 'vast', 'amounts', 'of', 'data', 'and', 'learning', 'from', 'it', 'in', 'ways', 'that', 'were', 'not', 'possible', 'before', 'as', 'a', 'result', 'ai', 'is', 'increasingly', 'being', 'used', 'in', 'a', 'wide', 'range', 'of', 'industries', 'from', 'healthcare', 'to', 'finance', 'to', 'transportation', 'and', 'its', 'impact', 'is', 'only', 'set', 'to', 'grow', 'in', 'the', 'years', 'to', 'come']\n",
    "print(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(to,)          7\n",
      "(of,)          6\n",
      "(the,)         4\n",
      "(in,)          4\n",
      "(learning,)    3\n",
      "              ..\n",
      "(humans,)      1\n",
      "(rise,)        1\n",
      "(advent,)      1\n",
      "(deep,)        1\n",
      "(come,)        1\n",
      "Name: count, Length: 79, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# unigrams: n=1\n",
    "unigrams = (pd.Series(nltk.ngrams(tokens, 1)).value_counts()) \n",
    "print(unigrams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '# of Occurances')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unigrams[0:10].sort_values().plot.barh(color='lightsalmon', width=.9, figsize=(12, 8))\n",
    "plt.title('10 Most Frequently Occuring Unigrams')\n",
    "plt.ylabel('Unigram')\n",
    "plt.xlabel('# of Occurances')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(that, were)             2\n",
      "(the, rise)              1\n",
      "(increasingly, being)    1\n",
      "(ai, is)                 1\n",
      "(result, ai)             1\n",
      "                        ..\n",
      "(tasks, that)            1\n",
      "(complex, tasks)         1\n",
      "(perform, complex)       1\n",
      "(to, perform)            1\n",
      "(to, come)               1\n",
      "Name: count, Length: 105, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# bigrams: n=2\n",
    "bigrams = (pd.Series(nltk.ngrams(tokens, 2)).value_counts()) \n",
    "print(bigrams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '# of Occurances')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bigrams[0:10].sort_values().plot.barh(color='skyblue', width=.9, figsize=(12, 8))\n",
    "plt.title('10 Most Frequently Occuring Bigrams')\n",
    "plt.ylabel('Bigram')\n",
    "plt.xlabel('# of Occurances')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(the, rise, of)              1\n",
      "(even, more, powerful)       1\n",
      "(ai, is, increasingly)       1\n",
      "(result, ai, is)             1\n",
      "(a, result, ai)              1\n",
      "                            ..\n",
      "(that, were, once)           1\n",
      "(tasks, that, were)          1\n",
      "(complex, tasks, that)       1\n",
      "(perform, complex, tasks)    1\n",
      "(years, to, come)            1\n",
      "Name: count, Length: 105, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# trigrams: n=3\n",
    "trigrams = (pd.Series(nltk.ngrams(tokens, 3)).value_counts()) \n",
    "print(trigrams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "nlp_course_env",
   "language": "python",
   "name": "nlp_course_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}