Scientific Article Hybrid Search System

Project Overview

This project implements a hybrid search system for scientific articles, combining traditional full-text search with modern embedding-based semantic search. The system is designed to provide more relevant and diverse search results by leveraging the strengths of both search methods.

Features

• Full-text search using Elasticsearch
• Semantic search using sentence embeddings (SentenceTransformer)
• Hybrid search combining both methods
• Evaluation metrics (MAP and NDCG) for search quality assessment
• Parameter optimization for improved performance
• Simple web interface for user interaction

Technologies Used

• Python 3.8+
• Elasticsearch
• Flask
• SentenceTransformer
• pandas
• scikit-learn
• NumPy

Project Structure

scientific_article_search/
│
├── data/
│   ├── train_data.csv
│   └── test_data.csv
│
├── src/
│   ├── data_preprocessing.py
│   ├── full_text_search.py
│   ├── semantic_search.py
│   ├── hybrid_search.py
│   ├── evaluation.py
│   └── app.py
│
├── templates/
│   └── index.html
│
├── requirements.txt
└── README.md

Installation and Setup

1. Clone the repository:

   git clone https://github.com/yourusername/scientific_article_search.git
   cd scientific_article_search

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows, use: venv\Scripts\activate

3. Install the required packages:

   pip install -r requirements.txt

4. Install and run Elasticsearch (follow instructions from the official Elasticsearch documentation)

5. Prepare the data:

   python src/data_preprocessing.py

Usage

1. Run the Flask application:

   python src/app.py

2. Open a web browser and navigate to http://localhost:5000

3. Enter a search query in the provided input field and click "Search" to see the results

Evaluation and Optimization

To evaluate and optimize the search system:

python src/evaluation.py

This script will output the initial performance metrics and the optimized parameters for the hybrid search system.

Future Improvements

• Implement more advanced NLP techniques (e.g., named entity recognition, topic modeling)
• Expand the dataset to cover a broader range of scientific domains
• Develop a more sophisticated user interface with advanced search options
• Implement user feedback mechanisms to continuously improve search results
• Explore cloud deployment options for scalability

Contributing

Contributions to this project are welcome. Please fork the repository and submit a pull request with your proposed changes.

License

This project is licensed under the MIT License.