README.md

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title: Word Sense Disambiguation
emoji: 🤖
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false

👋 Hi, I'm Gunjankumar Nitin Choudhari

🎯 About Me

I'm a B.Tech student at Ramrao Adik Institute of Technology, specializing in Computer Engineering with a focus on Data Science. With a strong CGPA of 9.40/10, I'm passionate about building intelligent systems and contributing to technological innovation.

🎓 Education

• B.Tech in Computer Engineering (Data Science)
  Ramrao Adik Institute of Technology, D. Y. Patil Deemed to be University
  CGPA: 9.40/10 | 2022 - Present

🛠️ Tech Stack

• Languages: Python, Java, C, SQL, NoSQL, HTML, CSS, JavaScript
• ML/AI: TensorFlow, Scikit-learn, LLMs, Generative AI
• Web: Flask, JSP, Servlets
• Mobile: Flutter, Dart
• Tools: Git, Power BI, VS Code, Jupyter, LaTeX, Figma
• Cloud: Oracle Cloud, AWS
• Databases: MySQL, MongoDB

🏆 Certifications

• Oracle Cloud Infrastructure 2024 Generative AI Certified Professional
• Intel Unnati Training on AI
• Data Science for Engineers (NPTEL)
• Programming in Python (NPTEL)
• Alteryx Machine Learning Fundamentals
• Data Structure and Algorithms – Internshala
• PowerBI for Beginners - Simplilearn

🚀 Featured Projects

1. AgrowAssist

Smart Agricultural Assistance System with ML-powered crop recommendations

• 35% improved prediction accuracy
• Flutter mobile app with TensorFlow Lite
• Real-time disease detection
• Power BI data visualization

2. AskDB

Natural Language to SQL Query Converter

• LLM-powered query conversion
• Flask backend with MySQL integration
• Responsive Bootstrap UI
• Efficient API communication

3. LESK BERT WSD

Advanced Word Sense Disambiguation System

• BERT embeddings integration
• Enhanced Lesk algorithm
• Real-time feedback system
• Flask web interface

💼 Professional Experience

Python Data Analyst Intern @ SPRINGBOARD

• Implemented ML algorithms improving prediction accuracy by 25%
• Created interactive Power BI dashboards
• Enhanced data visualization efficiency by 30%

Java Development Trainee @ IT-Networkz Infosystem

• Developed MVC architecture web applications
• Optimized database operations with JDBC
• Improved performance speed by 30%

🎯 Leadership & Activities

• Head of Design at CSI, RAIT
• Co-Head Design Officer at CSI, RAIT
• Co-Chief Design Officer at Social Wing, RAIT
• NSS Volunteer (2023-2024)
• Technical Content Creator on YouTube

📊 GitHub Stats

🌱 Currently Learning

• Advanced Generative AI
• Cloud Architecture
• System Design
• DevOps practices

🎯 Goals

• Contribute to open-source projects
• Master full-stack development
• Build impactful AI applications
• Learn cloud technologies

⚡ Fun Fact

When I'm not coding, you'll find me playing football, cricket, or creating graphic designs!

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⭐️ From Gunjankumar55

LESK BERT WSD - Advanced Word Sense Disambiguation

An advanced Word Sense Disambiguation (WSD) system that combines the Lesk algorithm with BERT embeddings for improved accuracy in determining word meanings from context.

🚀 Features

• Enhanced Lesk Algorithm: Improved version of the traditional Lesk algorithm
• BERT Integration: Uses BERT embeddings for better context understanding
• Interactive Web Interface: User-friendly Flask-based web application
• Real-time Feedback System: Learn from user corrections to improve accuracy
• Context-Aware Processing: Considers surrounding words and phrases
• Multiple Sense Support: Handles words with multiple meanings effectively

🛠️ Technical Stack

• Backend: Flask, Python 3.9
• NLP: NLTK, BERT Transformers
• Frontend: HTML, CSS, JavaScript
• Deployment: Docker, Hugging Face Spaces
• Version Control: Git

📋 Prerequisites

• Python 3.9+
• pip (Python package manager)
• Docker (for containerization)

🚀 Quick Start

1. Clone the repository:

git clone https://github.com/Gunjankumar55/LESK_BERT_WSD.git
cd LESK_BERT_WSD

2. Install dependencies:

pip install -r requirements.txt

3. Run the application:

python app.py

4. Access the web interface at http://localhost:5000

💡 Usage

1. Enter a sentence containing an ambiguous word
2. The system automatically detects potential ambiguous words
3. Select a word to disambiguate
4. View detailed results including:
   • Word definitions
   • Example usage
   • Confidence scores
   • Alternative meanings

🎯 Project Structure

LESK_BERT_WSD/
├── app.py              # Main application file
├── requirements.txt    # Project dependencies
├── Dockerfile         # Docker configuration
├── templates/         # HTML templates
└── static/           # Static assets

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📝 License

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

👨‍💻 Author

Gunjankumar Choudhari

• GitHub: @Gunjankumar55
• LinkedIn: Gunjankumar Choudhari
• Portfolio: Gunjan Portfolio

🙏 Acknowledgments

• NLTK team for the excellent NLP tools
• Hugging Face for BERT implementation
• Flask team for the web framework