# AWS CORD-19 Search: A Neural Search Engine for COVID-19 Literature

arXiv: https://arxiv.org/abs/2007.09186 · HTML: https://mlsanity.com/paper/2007.09186/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact](https://mlsanity.com/paper/2106.02359/) (2021)
- [Open-Domain Question-Answering for COVID-19 and Other Emergent Domains](https://mlsanity.com/paper/2110.06962/) (2021)
- [Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good](https://mlsanity.com/paper/2305.05471/) (2023)
- [Neural Entity Recognition with Gazetteer based Fusion](https://mlsanity.com/paper/2105.13225/) (2021)

## Abstract (arXiv)

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.