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

  • AgAsk: An Agricultural conversational search agent for answering comprehensive questions.
  • 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.

Collections and datasets