Finding answers to COVID-19-specific questions: An information retrieval system based on latent keywords and adapted TF-IDF
Journal of Information Science, 2024. doi:10.1177/01655515221110995
Abstract
The scientific community has reacted to the COVID-19 outbreak by producing a high number of literary works that are helping us to understand a variety of topics related to the pandemic from different perspectives. This paper presents an information retrieval system based on latent keywords and an adapted TF-IDF scheme for answering COVID-19-specific questions.
Related work
- Media Bias Within Information Disorder: Bridging Two Research Communities Through a Systematic ReviewInDor Workshop @ LREC 2026, 2026
- From Co-Pilots to Co-Workers: A Formal Typology of Human–Agent Collaboration in OrganizationsIEEE Conference on Artificial Intelligence (CAI) 2026, 2026
- The Epistemic Limits of NLP Models in Media Bias Detection: Toward a Framework for Context-Aware and Reflexive AI SystemsIEEE Conference on Artificial Intelligence (CAI) 2026, 2026
- A benchmark of expert-level academic questions to assess AI capabilitiesNature, 2026