Searching for scientific evidence in a pandemic : An overview of TREC-COVID
Copyright © 2021. Published by Elsevier Inc..
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.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2021 |
|---|---|
| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:121 |
|---|---|
| Enthalten in: |
Journal of biomedical informatics - 121(2021) vom: 01. Sept., Seite 103865 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Roberts, Kirk [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
COVID-19 |
|---|
| Anmerkungen: |
Date Completed 09.09.2021 Date Revised 21.12.2022 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1016/j.jbi.2021.103865 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM327917571 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM327917571 | ||
| 003 | DE-627 | ||
| 005 | 20231225201840.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.jbi.2021.103865 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1093.xml |
| 035 | |a (DE-627)NLM327917571 | ||
| 035 | |a (NLM)34245913 | ||
| 035 | |a (PII)S1532-0464(21)00194-5 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Roberts, Kirk |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Searching for scientific evidence in a pandemic |b An overview of TREC-COVID |
| 264 | 1 | |c 2021 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 09.09.2021 | ||
| 500 | |a Date Revised 21.12.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2021. Published by Elsevier Inc. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Review | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a Information retrieval | |
| 650 | 4 | |a Pandemics | |
| 650 | 4 | |a TREC-COVID | |
| 700 | 1 | |a Alam, Tasmeer |e verfasserin |4 aut | |
| 700 | 1 | |a Bedrick, Steven |e verfasserin |4 aut | |
| 700 | 1 | |a Demner-Fushman, Dina |e verfasserin |4 aut | |
| 700 | 1 | |a Lo, Kyle |e verfasserin |4 aut | |
| 700 | 1 | |a Soboroff, Ian |e verfasserin |4 aut | |
| 700 | 1 | |a Voorhees, Ellen |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Lucy Lu |e verfasserin |4 aut | |
| 700 | 1 | |a Hersh, William R |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of biomedical informatics |d 2001 |g 121(2021) vom: 01. Sept., Seite 103865 |w (DE-627)NLM112821766 |x 1532-0480 |7 nnns |
| 773 | 1 | 8 | |g volume:121 |g year:2021 |g day:01 |g month:09 |g pages:103865 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.jbi.2021.103865 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 121 |j 2021 |b 01 |c 09 |h 103865 | ||