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Multi-task Character-Level Attentional Networks for Medical Concept Normalization

文献类型：期刊论文

作者Niu, Jinghao1,2; Yang, Yehui1,2; Zhang, Siheng1,2; Sun, Zhengya1,2; Zhang, Wensheng1,2
刊名NEURAL PROCESSING LETTERS
出版日期2019-06-01
卷号49期号:3页码:1239-1256
关键词Convolutional neural network Multi-task learning Medical concept normalization
ISSN号1370-4621
DOI10.1007/s11063-018-9873-x
通讯作者Zhang, Wensheng(zhangwenshengia@hotmail.com)
英文摘要Recognizing standard medical concepts in the colloquial text is significant for kinds of applications such as the medical question answering system. Recently, word-level neural network methods, which can learn complex informal expression features, achieved remarkable performance on this task. However, they have two main limitations: (1) Existing word-level methods cannot learn character structure features inside words and suffer from "Out-of-vocabulary" (OOV) words, which are common in noisy colloquial text. (2) Since these methods handle the normalization task as a classification issue, concept phrases are represented by category labels. Hence theword morphological information inside the concept is lost. In this work, we present a multi-task character-level attentional network model for medical concept normalization. Specifically, the character-level encoding scheme of our model can alleviate the OOV word problem. The attention mechanism can effectively exploit thewordmorphological information through multi-task training. It generates higher attention weights on domain-related positions in the text sequence, helping the downstream convolution focus on the characters that are related to medical concepts. To test our model, we first introduce a labeled Chinese dataset (overall 314,991 records) for this task. Other two realworld English datasets are also used. Our model outperforms state-of-the-art methods on all three datasets. Besides, by adding four types noises to the datasets, we validate the robustness of our model against common noises in the colloquial text.
资助项目National Natural Science Foundation of China[61472423] ; National Natural Science Foundation of China[61432008] ; National Natural Science Foundation of China[U1636220] ; Beijing Municipal Natural Science Foundation[4172063]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000483206800024
出版者SPRINGER
资助机构National Natural Science Foundation of China ; Beijing Municipal Natural Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/27202]  
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者Zhang, Wensheng
作者单位1.Univ Chinese Acad Sci, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Niu, Jinghao,Yang, Yehui,Zhang, Siheng,et al. Multi-task Character-Level Attentional Networks for Medical Concept Normalization[J]. NEURAL PROCESSING LETTERS,2019,49(3):1239-1256.
APA Niu, Jinghao,Yang, Yehui,Zhang, Siheng,Sun, Zhengya,&Zhang, Wensheng.(2019).Multi-task Character-Level Attentional Networks for Medical Concept Normalization.NEURAL PROCESSING LETTERS,49(3),1239-1256.
MLA Niu, Jinghao,et al."Multi-task Character-Level Attentional Networks for Medical Concept Normalization".NEURAL PROCESSING LETTERS 49.3(2019):1239-1256.

入库方式： OAI收割

来源：自动化研究所

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