Named Entity Recognition (NER) of Text (is pending)

Overview

This project focuses on implementing Named Entity Recognition (NER) to extract meaningful entities from text data. NER is a subtask of Natural Language Processing (NLP) that identifies and categorizes entities such as names, organizations, locations, dates, and more.

Features

• Preprocessing of raw text data.
• Implementation of NER using state-of-the-art models.
• Support for custom entity types.
• Evaluation metrics for model performance.

Installation

1. Clone the repository:

   git clone https://github.com/your-username/ner-paragraph.git

2. Navigate to the project directory:

   cd ner-paragraph

3. Install dependencies:

   pip install -r requirements.txt

Usage

1. Prepare your input text file.
2. Run the NER script:

   python ner.py --input input.txt --output output.json

3. View the extracted entities in the output file.

Example

Input:

Barack Obama was born in Hawaii and served as the 44th President of the United States.

Output:

{
  "PERSON": ["Barack Obama"],
  "LOCATION": ["Hawaii", "United States"],
  "ORDINAL": ["44th"]
}

Technologies

• Python
• SpaCy / Hugging Face Transformers
• NLTK / Custom Preprocessing

Contributing

Contributions are welcome! Please fork the repository and submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For questions or suggestions, feel free to reach out:

• Email: tuyenvt455@gmail.com
• GitHub: your-username