TY - GEN
T1 - Extracting a Knowledge Base of Mechanisms from COVID-19 Papers
AU - Hope, Tom
AU - Amini, Aida
AU - Wadden, David
AU - van Zuylen, Madeleine
AU - Parasa, Sravanthi
AU - Horvitz, Eric
AU - Weld, Daniel
AU - Schwartz, Roy
AU - Hajishirzi, Hannaneh
N1 - Publisher Copyright: © 2021 Association for Computational Linguistics.
PY - 2021
Y1 - 2021
N2 - The COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge. We pursue the construction of a knowledge base (KB) of mechanisms—a fundamental concept across the sciences, which encompasses activities, functions and causal relations, ranging from cellular processes to economic impacts. We extract this information from the natural language of scientific papers by developing a broad, unified schema that strikes a balance between relevance and breadth. We annotate a dataset of mechanisms with our schema and train a model to extract mechanism relations from papers. Our experiments demonstrate the utility of our KB in supporting interdisciplinary scientific search over COVID-19 literature, outperforming the prominent PubMed search in a study with clinical experts. Our search engine, dataset and code are publicly available.
AB - The COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge. We pursue the construction of a knowledge base (KB) of mechanisms—a fundamental concept across the sciences, which encompasses activities, functions and causal relations, ranging from cellular processes to economic impacts. We extract this information from the natural language of scientific papers by developing a broad, unified schema that strikes a balance between relevance and breadth. We annotate a dataset of mechanisms with our schema and train a model to extract mechanism relations from papers. Our experiments demonstrate the utility of our KB in supporting interdisciplinary scientific search over COVID-19 literature, outperforming the prominent PubMed search in a study with clinical experts. Our search engine, dataset and code are publicly available.
UR - http://www.scopus.com/inward/record.url?scp=85123637299&partnerID=8YFLogxK
U2 - 10.18653/v1/2021.naacl-main.355
DO - 10.18653/v1/2021.naacl-main.355
M3 - منشور من مؤتمر
T3 - NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference
SP - 4489
EP - 4503
BT - NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics
PB - Association for Computational Linguistics (ACL)
T2 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021
Y2 - 6 June 2021 through 11 June 2021
ER -
