Part 6 Named Entity Recognition.ipynb

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    "**Named Entity Recognition (NER)** is a technique in NLP used to locate and classify key information in text into predefined categories such as **names of people, organizations, locations, dates,** and **quantities**. By identifying and categorizing these \"named entities,\" NER helps extract useful information from text data, which is essential for tasks like information retrieval, question answering, and text summarization.\r\n",
    "\r\n",
    "### Categories Commonly Detected in NER:\r\n",
    "- **Person**: Names of people (e.g., \"Elon Musk\")\r\n",
    "- **Organization**: Companies, agencies, institutions (e.g., \"Google\", \"United Nations\")\r\n",
    "- **Location**: Geographical locations (e.g., \"Paris\", \"Mount Everest\")\r\n",
    "- **Date/Time**: Specific dates or times (e.g., \"January 1, 2022\", \"10:00 AM\")\r\n",
    "- **Money/Percent/Quantity**: Financial values, percentages, or measurements (e.g., \"$1 million\", \"20%\", \"for more advanced entity recognition and custom entity training."
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      "[nltk_data] Downloading package maxent_ne_chunker_tab to\n",
      "[nltk_data]     C:\\Users\\sayan\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package maxent_ne_chunker_tab is already up-to-date!\n",
      "[nltk_data] Downloading package words to\n",
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      "[nltk_data]   Package words is already up-to-date!\n"
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    "nltk.download('maxent_ne_chunker_tab')\n",
    "nltk.download('words')"
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      "(S\n",
      "  (PERSON Apple/NNP)\n",
      "  (ORGANIZATION Inc./NNP)\n",
      "  was/VBD\n",
      "  founded/VBN\n",
      "  by/IN\n",
      "  (PERSON Steve/NNP Jobs/NNP)\n",
      "  in/IN\n",
      "  (GPE Cupertino/NNP)\n",
      "  ,/,\n",
      "  (GPE California/NNP)\n",
      "  ,/,\n",
      "  in/IN\n",
      "  1976/CD\n",
      "  ./.\n",
      "  In/IN\n",
      "  2021/CD\n",
      "  ,/,\n",
      "  the/DT\n",
      "  company/NN\n",
      "  was/VBD\n",
      "  valued/VBN\n",
      "  at/IN\n",
      "  over/IN\n",
      "  $/$\n",
      "  2/CD\n",
      "  trillion/CD\n",
      "  ./.)\n"
     ]
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   "source": [
    "import nltk\n",
    "from nltk import word_tokenize, pos_tag, ne_chunk\n",
    "\n",
    "# Sample text\n",
    "text = \"Apple Inc. was founded by Steve Jobs in Cupertino, California, in 1976. In 2021, the company was valued at over $2 trillion.\"\n",
    "\n",
    "# Tokenize and tag parts of speech\n",
    "words = word_tokenize(text)\n",
    "tagged_words = pos_tag(words)\n",
    "\n",
    "# Perform Named Entity Recognition\n",
    "named_entities = ne_chunk(tagged_words)\n",
    "\n",
    "# Display named entities\n",
    "print(named_entities)"
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