论文标题
实时回答有关Covid-19的问题
Answering Questions on COVID-19 in Real-Time
论文作者
论文摘要
新颖的冠状病毒最近爆发对世界造成了严重破坏，研究人员正在努力有效地对抗它。战斗很困难的原因之一是由于缺乏信息和知识。在这项工作中，我们概述了通过创建Covidask来缩小这种知识真空的努力，这是一个问题回答（QA）系统，该系统结合了生物医学文本挖掘和QA技术，以实时提供问题的答案。我们的系统还利用信息检索方法（IR）方法提供了与QA模型互补的实体级答案。 Covidask的评估是通过使用手动创建的名为COVID-19问题的数据集进行的，该数据基于来自各种来源的信息，包括CDC和WHO。我们希望我们的系统能够帮助研究人员不仅为Covid-19，还为未来的大流行技术寻找知识和信息。
The recent outbreak of the novel coronavirus is wreaking havoc on the world and researchers are struggling to effectively combat it. One reason why the fight is difficult is due to the lack of information and knowledge. In this work, we outline our effort to contribute to shrinking this knowledge vacuum by creating covidAsk, a question answering (QA) system that combines biomedical text mining and QA techniques to provide answers to questions in real-time. Our system also leverages information retrieval (IR) approaches to provide entity-level answers that are complementary to QA models. Evaluation of covidAsk is carried out by using a manually created dataset called COVID-19 Questions which is based on information from various sources, including the CDC and the WHO. We hope our system will be able to aid researchers in their search for knowledge and information not only for COVID-19, but for future pandemics as well.
