ShanghaiTech University Knowledge Management System
Topic Judgment Helps Question Similarity Prediction in Medical FAQ Dialogue Systems | |
2019-11 | |
会议录名称 | 2019 INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW)
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ISSN | 2375-9232 |
页码 | 966-972 |
发表状态 | 已发表 |
DOI | 10.1109/ICDMW.2019.00140 |
摘要 | As a prominent application of artificial intelligence in healthcare, medical dialogue systems have a promising future. In this paper, we built a medical Frequently Asked Questions (FAQ) dialogue system using an XGBoost classifier based on handcrafted features. Moreover, we propose to improve accuracy in domain-specific information retrieval by adding a topic judgment module to general sentence similarity prediction. As the size of the labeled medical corpus in Chinese is very limited, we built a corpus of Chinese medical FAQ from mvyxws https://www.mvyxws.com/ and annotated a small part of it. We applied our topic judgment module to the XGBoost model, a general sentence similarity model, and Baidu short text similarity API. Testing on two datasets, the models can be improved by adding the topic judgment in most cases. |
会议地点 | Beijing, China |
会议日期 | 8-11 Nov. 2019 |
URL | 查看原文 |
收录类别 | EI |
语种 | 英语 |
出版者 | IEEE Computer Society |
EI入藏号 | 20200608123342 |
EI主题词 | Natual Language Processing |
EI分类号 | Data Processing and Image Processing:723.2 |
原始文献类型 | Conference article (CA) |
来源库 | IEEE |
引用统计 | 正在获取...
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文献类型 | 会议论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/104297 |
专题 | 信息科学与技术学院 信息科学与技术学院_硕士生 信息科学与技术学院_PI研究组_郑杰组 |
通讯作者 | Jie Zheng |
作者单位 | 1.School of Information Science and Technology, ShanghaiTech University 2.School of Physics, University of Electronic Science and Technology China 3.R & D, XiaoDuoAI |
第一作者单位 | 信息科学与技术学院 |
通讯作者单位 | 信息科学与技术学院 |
第一作者的第一单位 | 信息科学与技术学院 |
推荐引用方式 GB/T 7714 | Lin Zhu,Xinnan Dai,Qihao Huang,et al. Topic Judgment Helps Question Similarity Prediction in Medical FAQ Dialogue Systems[C]:IEEE Computer Society,2019:966-972. |
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