🔍 Named 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:

• 🏢 Organizations
• 🌍 Locations
• 👤 Person Names
• 📅 Dates
• 🏷️ Other important named entities

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

🔗 Try it out here: NER ChatBot

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⚙️ Installation

1️⃣ Clone the Repository:

git clone https://github.com/SimranShaikh20/Name-Entity-Recognition.git
cd Name-Entity-Recognition

2️⃣ Install Dependencies:

Ensure all required dependencies are installed:

pip install -r requirements.txt

3️⃣ 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

📂 Open the Jupyter Notebook:

Launch the Jupyter Notebook to run the project:

jupyter notebook NameEntityRecognition.ipynb

🎯 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
└── app.py                        #streamlit app

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📚 Libraries Used

• 🧠 SpaCy: For performing Named Entity Recognition (NER) and other NLP tasks.
• 🖥️ Streamlit: For creating a frontend application.

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🔮 Future Enhancements

🚀 Planned Improvements:

• 🔹 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.

🚀 Happy Coding! 🎯