{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entity: L V | Type: ('Let', 'VB')\n",
      "Entity: ' P | Type: (\"'s\", 'POS')\n",
      "Entity: m N | Type: ('meet', 'NN')\n",
      "Entity: f I | Type: ('for', 'IN')\n",
      "Entity: l N | Type: ('lunch', 'NN')\n",
      "Entity: t N | Type: ('tomorrow', 'NN')\n",
      "Entity: a I | Type: ('at', 'IN')\n",
      "Entity: 1 C | Type: ('12', 'CD')\n",
      "Entity: P N | Type: ('PM', 'NNP')\n",
      "Entity: a I | Type: ('at', 'IN')\n",
      "Entity: t D | Type: ('the', 'DT')\n",
      "Entity: Italian | Type: (GPE Italian/JJ)\n",
      "Entity: r N | Type: ('restaurant', 'NN')\n",
      "Entity: o I | Type: ('on', 'IN')\n",
      "Entity: Main Street | Type: (FACILITY Main/NNP Street/NNP)\n",
      "Entity: . . | Type: ('.', '.')\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from nltk import ne_chunk, pos_tag\n",
    "from nltk.tokenize import word_tokenize\n",
    "\n",
    "# Sample text for demonstration\n",
    "text = \"Let's meet for lunch tomorrow at 12 PM at the Italian restaurant on Main Street.\"\n",
    "\n",
    "# Tokenize the text into words\n",
    "tokens = word_tokenize(text)\n",
    "\n",
    "# Apply NER using NLTK's pre-trained models\n",
    "ner_tags = ne_chunk(pos_tag(tokens))\n",
    "\n",
    "# Print the named entities\n",
    "for chunk in ner_tags:\n",
    "    if hasattr(chunk, 'label'):\n",
    "        print(f\"Entity: {' '.join(c[0] for c in chunk)} | Type: {chunk}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entity: Lunch Tomorrow | Type: PERSON\n",
      "Entity: Italian | Type: GPE\n",
      "Entity: Main Street | Type: FACILITY\n"
     ]
    }
   ],
   "source": [
    "# Apply NER using NLTK's pre-trained models\n",
    "ner_tags = ne_chunk(pos_tag(tokens))\n",
    "\n",
    "# Print the named entities\n",
    "for chunk in ner_tags:\n",
    "    if hasattr(chunk, 'label'):\n",
    "        print(f\"Entity: {' '.join(c[0] for c in chunk)} | Type: {chunk.label()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}