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AWS CORD-19 Search: A Neural Search Engine for COVID-19 Literature
July 17, 2020 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Parminder Bhatia, Lan Liu, Kristjan Arumae, Nima Pourdamghani, Suyog Deshpande, Ben Snively, Mona Mona, Colby Wise, George Price, Shyam Ramaswamy, Xiaofei Ma, Ramesh Nallapati, Zhiheng Huang, Bing Xiang, Taha Kass-Hout
arXiv ID
2007.09186
Category
cs.IR: Information Retrieval
Citations
8
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Coronavirus disease (COVID-19) has been declared as a pandemic by WHO with thousands of cases being reported each day. Numerous scientific articles are being published on the disease raising the need for a service which can organize, and query them in a reliable fashion. To support this cause we present AWS CORD-19 Search (ACS), a public, COVID-19 specific, neural search engine that is powered by several machine learning systems to support natural language based searches. ACS with capabilities such as document ranking, passage ranking, question answering and topic classification provides a scalable solution to COVID-19 researchers and policy makers in their search and discovery for answers to high priority scientific questions. We present a quantitative evaluation and qualitative analysis of the system against other leading COVID-19 search platforms. ACS is top performing across these systems yielding quality results which we detail with relevant examples in this work.
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