文献类型：期刊文献

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] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China; [2] School of Science, East China University of Science and Technology, Shanghai, 200237, China; [3] Shanghai Hospital Development Center, Shanghai, 200040, China

年份：2018

起止页码：1031

外文期刊名：Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

收录：EI(收录号：20200402494)

语种：英文

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-theart methods both on English and Chinese corpora of symptom and disease pairs. ? 2018 IEEE.