This project develops an information retrieval system to identify relevant COVID-19 documents. It aims to aid public understanding, support clinicians, and contribute to global pandemic research.

• CORD-19: Round 5 dataset from Kaggle, including 128K research papers in JSON and metadata formats.

1. Data Pre-processing: Transform raw documents into a searchable format.
2. Document Indexing: Utilize Elasticsearch for efficient indexing and retrieval.
3. Document Retrieval: Implement query schemes for optimal document fetching.
4. Re-ranking: Apply BERT-based models to enhance document relevance ranking.

• Utilized Trec_eval for evaluation.
• Achieved significant precision improvements with various query combinations and re-ranking techniques.
• Best results observed with COVID-SciBERT model in re-ranking phase.

Instructions for setting up and running the system are provided, detailing steps from data pre-processing to document re-ranking.

Refer to the LICENSE file for licensing details.

For further information and detailed methodology, refer to the full report.