TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection
Ellen Voorhees (National Institute of Standards, Technology) and, Tasmeer Alam (National Institute of Standards, Technology), Steven, Bedrick (Oregon Health, Science University), Dina Demner-Fushman (U.S., National Library of Medicine), William R Hersh (Oregon Health, Science
TL;DR
TREC-COVID develops a specialized test collection to evaluate information retrieval systems tailored for rapidly evolving biomedical literature during a pandemic, aiding research in pandemic-related search technologies.
Contribution
It introduces a new pandemic-specific test collection and infrastructure to support research on information retrieval in rapidly changing scientific literature.
Findings
Created a COVID-19 literature test collection
Captured evolving information needs during the pandemic
Supported development of pandemic search technologies
Abstract
TREC-COVID is a community evaluation designed to build a test collection that captures the information needs of biomedical researchers using the scientific literature during a pandemic. One of the key characteristics of pandemic search is the accelerated rate of change: the topics of interest evolve as the pandemic progresses and the scientific literature in the area explodes. The COVID-19 pandemic provides an opportunity to capture this progression as it happens. TREC-COVID, in creating a test collection around COVID-19 literature, is building infrastructure to support new research and technologies in pandemic search.
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Taxonomy
TopicsBiomedical Text Mining and Ontologies · Semantic Web and Ontologies · Topic Modeling