Name Entity Recognition (NER) Project

Description

This project demonstrates Named Entity Recognition (NER) using SpaCy, a powerful Natural Language Processing (NLP) library. NER identifies and classifies key elements in a text, such as names of persons, organizations, locations, dates, and more. By leveraging SpaCy’s pre-trained models, this project provides an easy-to-use interface to analyze text and extract named entities. This capability is crucial for tasks such as document analysis, information retrieval, and chatbot development.

The goal of this project is to showcase the simplicity of implementing NER with SpaCy and its potential as a foundation for more advanced NLP applications.

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Application Link: NER ChatBot

Installation

1. Clone the Repository:

   git clone https://github.com/SimranShaikh20/NameEntityRecognition.git
   cd NameEntityRecognition

2. Install Dependencies: Ensure all required dependencies are installed by running:

   pip install -r requirements.txt

3. Download SpaCy Language Model: Download the SpaCy English language model required for NER analysis:

   python -m spacy download en_core_web_sm

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Usage

1. Open the Jupyter Notebook: Launch the Jupyter Notebook to run the project:

   jupyter notebook NameEntityRecognition.ipynb

2. Follow the Notebook Cells:

   • Provide your text input for NER analysis.
   • Execute the cells to run the NER process.
   • View and interpret the extracted named entities.

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File Structure

NameEntityRecognition/
├── NameEntityRecognition.ipynb  # Main Jupyter Notebook
├── requirements.txt              # List of dependencies
├── README.md                     # Project documentation
└── LICENSE                       # License file

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Libraries Used

• SpaCy: For performing Named Entity Recognition (NER) and other NLP tasks.
• Streamlit: For creating fontend application .

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Future Enhancements

• Add functionality for custom NER model training with user-provided datasets.
• Enhance visualization of named entities with interactive charts.
• Integrate the project into a web application for real-time NER analysis.
• Support additional languages by downloading and integrating other SpaCy language models.

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Author

This project was created by Simran Shaikh.

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License

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

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Acknowledgments

• Special thanks to the SpaCy documentation and community for their extensive resources and support.
• Inspired by the simplicity and versatility of NLP tasks in SpaCy.