文献类型：会议论文

英文题名：An Attention-based Bi-GRU-CapsNet Model for Hypernymy Detection between Compound Entities

作者：Wang, Qi[1];Xu, Chenming[2];Zhou, Yangming[1];Ruan, Tong[1];Gao, Daqi[1];He, Ping[3]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Sch Sci, Shanghai 200237, Peoples R China;[3]Shanghai Hosp Dev Ctr, Shanghai 200040, Peoples R China

会议论文集：IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Human Genomics

会议日期：DEC 03-06, 2018

会议地点：Madrid, SPAIN

语种：英文

外文关键词：Hypernymy detection; compound entities; capsule network; attention mechanism; electronic health records

摘要：Named entities are usually composable and extensible. Typical examples are names of symptoms and diseases in medical areas. To distinguish these entities from general entities, we name them compound entities. In this paper, we present an attention-based Bi-GRU-CapsNet model to detect hypernymy relationship between compound entities. Our model consists of several important components. To avoid the out-of-vocabulary problem, English words or Chinese characters in compound entities are fed into the bidirectional gated recurrent units. An attention mechanism is designed to focus on the differences between two compound entities. Since there are some different cases in hypernymy relationship between compound entities, capsule network is finally employed to decide whether the hypernymy relationship exists or not. Experimental results demonstrate the advantages of our model over the state-of-the-art methods both on English and Chinese corpora of symptom and disease pairs.