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Searching for scientific evidence in a pandemic: An overview of TREC-COVID.
Roberts, Kirk; Alam, Tasmeer; Bedrick, Steven; Demner-Fushman, Dina; Lo, Kyle; Soboroff, Ian; Voorhees, Ellen; Wang, Lucy Lu; Hersh, William R.
  • Roberts K; University of Texas Health Science Center at Houston, Houston, TX, USA. Electronic address: kirk.roberts@uth.tmc.edu.
  • Alam T; Morgan State University, Baltimore, MD, USA.
  • Bedrick S; Oregon Health & Science University, Portland, OR, USA.
  • Demner-Fushman D; US National Library of Medicine, Bethesda, MD, USA.
  • Lo K; Allen Institute for AI, Seattle, WA, USA.
  • Soboroff I; National Institute of Standards and Technology, Gaithersburg, MD, USA.
  • Voorhees E; National Institute of Standards and Technology, Gaithersburg, MD, USA.
  • Wang LL; Allen Institute for AI, Seattle, WA, USA.
  • Hersh WR; Oregon Health & Science University, Portland, OR, USA.
J Biomed Inform ; 121: 103865, 2021 09.
Article in English | MEDLINE | ID: covidwho-1300864
ABSTRACT
We present an overview of the TREC-COVID Challenge, an information retrieval (IR) shared task to evaluate search on scientific literature related to COVID-19. The goals of TREC-COVID include the construction of a pandemic search test collection and the evaluation of IR methods for COVID-19. The challenge was conducted over five rounds from April to July 2020, with participation from 92 unique teams and 556 individual submissions. A total of 50 topics (sets of related queries) were used in the evaluation, starting at 30 topics for Round 1 and adding 5 new topics per round to target emerging topics at that state of the still-emerging pandemic. This paper provides a comprehensive overview of the structure and results of TREC-COVID. Specifically, the paper provides details on the background, task structure, topic structure, corpus, participation, pooling, assessment, judgments, results, top-performing systems, lessons learned, and benchmark datasets.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / COVID-19 Type of study: Experimental Studies Limits: Humans Language: English Journal: J Biomed Inform Journal subject: Medical Informatics Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / COVID-19 Type of study: Experimental Studies Limits: Humans Language: English Journal: J Biomed Inform Journal subject: Medical Informatics Year: 2021 Document Type: Article