Knowledge-Based Systems
Knowledge-Based Systems
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DOI：
10.4324/9781351109598-8
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发表时间：
2022
影响因子：
1.8
通讯作者：
Arantxa Otegi;Iñaki San;X. Saralegi;Anselmo Peñas;Borja Lozano;Eneko Agirre
中科院分区：
文献类型：
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作者：
Arantxa Otegi;Iñaki San;X. Saralegi;Anselmo Peñas;Borja Lozano;Eneko Agirre
世界各地的生物卫生专家都在致力于新冠病毒（COVID - 19）的研究。这种努力以一种使得有效获取新知识变得困难的速度产生了大量的科学出版物。因此，需要信息系统来帮助生物卫生专家获取、查阅和分析这些出版物。在这项工作中，我们对问答系统开发中涉及的变量进行了研究，该系统接收专家提出的一组关于新冠疾病（COVID - 19）及其致病病毒严重急性呼吸综合征冠状病毒2型（SARS - CoV - 2）的问题，并为每个问题提供一个按专家水平排序的答案列表。特别是，我们探讨了信息检索和答案提取步骤之间的相互关系。我们发现，基于召回率的文档检索，让神经答案提取模块扫描整个文档以找到最佳答案，这是一种比在提取答案片段之前依赖精确的段落检索更好的策略。
Biosanitary experts around the world are directing their efforts towards the study of COVID-19. This effort generates a large volume of scientific publications at a speed that makes the effective acquisition of new knowledge difficult. Therefore, Information Systems are needed to assist biosanitary experts in accessing, consulting and analyzing these publications. In this work we develop a study of the variables involved in the development of a Question Answering system that receives a set of questions asked by experts about the disease COVID-19 and its causal virus SARS-CoV-2, and provides a ranked list of expert-level answers to each question. In particular, we address the interrelation of the Information Retrieval and the Answer Extraction steps. We found that a recall based document retrieval that leaves to a neural answer extraction module the scanning of the whole documents to find the best answer is a better strategy than relying in a precise passage retrieval before extracting the answer span.