AWS CORD-19 Search: A Neural Search Engine for COVID-19 Literature
This provides a scalable solution for COVID-19 researchers and policymakers to search for answers to high-priority scientific questions, though it is incremental as it applies existing neural search methods to a new domain.
The authors tackled the problem of organizing and querying the rapidly growing COVID-19 literature by developing AWS CORD-19 Search (ACS), a neural search engine that supports natural language searches, and it performed top against other leading COVID-19 search platforms in evaluations.
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.