The ielab works on a diversity of research projects in the fields of information retrieval, data science, and health informatics. Research strengths include:
- Formal models of Information Retrieval: retrieval models, learning to rank, deep learning, user models, evaluation of information retrieval systems.
- Health Search and Health Data Science: models, systems, evaluation for tasks in consumer health search, clinical decision support, precision medicine, search for systematic review compilation, cohort selection for clinical trials.
- Domain-specific search: case law retrieval.
Current highlighted projects
GitHub repositories associated to ielab projects and initiatives
Tools, software and demonstrators
- searchrefiner: A Query Visualisation and Understanding tool for Systematic Reviews.
- querylab: A Query Visualisation and Understanding tool for Systematic Reviews.
- INST eval: Python implementation of the INST evaluation measure from Moffat et al.
- Relevation: Information Retrieval Relevance Judging System
- query generation: An annotator toolkit for creating manual queries from clinical decision support scenarios.
- NLTM: Implementation of Neural Translation Language Models, along with embeddings and experimental data/results, associated with the article by G. Zuccon, B. Koopman, P. Bruza, L. Azzopardi, “Integrating and Evaluating Neural Word Embeddings in Information Retrieval”, ADCS 2015.