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Participation in TREC 2020 COVID Track Using Continuous Active Learning
November 03, 2020 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Xue Jun Wang, Maura R. Grossman, Seung Gyu Hyun
arXiv ID
2011.01453
Category
cs.IR: Information Retrieval
Citations
2
Venue
arXiv.org
Last Checked
4 months ago
Abstract
We describe our participation in all five rounds of the TREC 2020 COVID Track (TREC-COVID). The goal of TREC-COVID is to contribute to the response to the COVID-19 pandemic by identifying answers to many pressing questions and building infrastructure to improve search systems [8]. All five rounds of this Track challenged participants to perform a classic ad-hoc search task on the new data collection CORD-19. Our solution addressed this challenge by applying the Continuous Active Learning model (CAL) and its variations. Our results showed us to be amongst the top scoring manual runs and we remained competitive within all categories of submissions.
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