论文标题
通过两个阶段神经匹配模型检索和排名简短的医疗问题
Retrieving and ranking short medical questions with two stages neural matching model
论文作者
论文摘要
由于移动网络技术的最新进展和医疗保健服务的高需求，互联网医院是一项不断增长的业务。在线医疗服务变得越来越流行和活跃。根据2018年的美国数据，有80％的互联网用户在网上询问了与健康相关的问题。许多数据以前所未有的速度和规模生成。医疗领域的这些代表性问题和答案是医疗数据挖掘的宝贵原始数据源。自动化的机器对这些数据量的解释提供了一个机会，可以帮助医生从信息检索和机器学习方法的角度回答与医疗有关的问题。在这项工作中，我们为查询级医学问题的语义匹配提出了一个新颖的两阶段框架。
Internet hospital is a rising business thanks to recent advances in mobile web technology and high demand of health care services. Online medical services become increasingly popular and active. According to US data in 2018, 80 percent of internet users have asked health-related questions online. Numerous data is generated in unprecedented speed and scale. Those representative questions and answers in medical fields are valuable raw data sources for medical data mining. Automated machine interpretation on those sheer amount of data gives an opportunity to assist doctors to answer frequently asked medical-related questions from the perspective of information retrieval and machine learning approaches. In this work, we propose a novel two-stage framework for the semantic matching of query-level medical questions.
