论文标题
AWS CORD-19搜索：COVID-19文献的神经搜索引擎
AWS CORD-19 Search: A Neural Search Engine for COVID-19 Literature
论文作者
论文摘要
冠状病毒疾病（Covid-19）被宣布为大流行，每天有成千上万的病例报告。许多科学文章都在发表有关这种疾病的发表，这增加了可以组织的服务，并以可靠的方式进行询问。为了支持这一原因，我们提出了AWS CORD-19搜索（ACS），这是公众，COVID-19特定的，神经搜索引擎，由多个机器学习系统提供动力，以支持基于自然语言的搜索。具有文档排名，通过排名，问题答案和主题分类等功能的ACS为COVID-19的研究人员和政策制定者提供了可扩展的解决方案，以搜索和发现，以解决高优先级科学问题的答案。我们对系统对其他领先的Covid-19搜索平台进行了定量评估和定性分析。 ACS在这些系统中表现出色，得出质量结果，我们将在这项工作中使用相关示例进行详细介绍。
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.
