TREC-COVID
By Ellen M. Voorhees, Tasmeer Alam, Steven Bedrick, Dina Demner-Fushman, William R. Hersh, Kyle Lo, Kirk Roberts, Ian Soboroff, Lucy Lu Wang. 2020
94 citations recorded by the source corpus.
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.
Subfields: Genetics, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Genetics
In Molecular Biology
- Rodrigo Nogueira
- Jimmy J. Lin
- Daniel S. Weld
- Lucy Lu Wang
- Iryna Gurevych
- Rodney Michael Kinney
- Oren Etzioni
- Kuansan Wang
Citations in the corpus
- CORD-19: The Covid-19 Open Research Dataset (2020, methodology): “To support novel use cases, CORD-19 will continue to invest in regular updates and new features.”
- Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset (2020, background): “Multi-stage search architectures represent the most common design for modern search engines, with work in academia dating back over a decade (Matveeva et al., 2006; Wang et al., 2011; Asadi and Lin, 2013).”
- MS MARCO: Benchmarking Ranking Models in the Large-Data Regime (2021, methodology): “In a meta-analysis of participating runs in the TREC-COVID challenge, Chen and Hersh [9] found the use of MS MARCO dataset for finetuning to be associated with higher retrieval performance.”
- BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models (2021, methodology): “Bio-Medical Bio-Medical TREC-COVID [65] 3 3-level —- —- 50 171,332 493.”