中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
A Hybrid Model for Named Entity Recognition on Chinese Electronic Medical Records

文献类型:期刊论文

作者Wang, Yu1,2,3; Sun, Yining1,2,3; Ma, Zuchang2,4; Gao, Lisheng2,4; Xu, Yang2,4
刊名ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING
出版日期2021-04-01
卷号20
关键词Named entity recognition Chinese electronic medical records neural networks hybrid models
ISSN号2375-4699
DOI10.1145/3436819
通讯作者Sun, Yining(ynsun@iim.ac.cn)
英文摘要Electronic medical records (EMRs) contain valuable information about the patients, such as clinical symptoms, diagnostic results, and medications. Named entity recognition (NER) aims to recognize entities from unstructured text, which is the initial step toward the semantic understanding of the EMRs. Extracting medical information from Chinese EMRs could be a more complicated task because of the difference between English and Chinese. Some researchers have noticed the importance of Chinese NER and used the recurrent neural network or convolutional neural network (CNN) to deal with this task. However, it is interesting to know whether the performance could be improved if the advantages of the RNN and CNN can be both utilized. Moreover, RoBERTa-WWM, as a pre-training model, can generate the embeddings with word-level features, which is more suitable for Chinese NER compared with Word2Vec. In this article, we propose a hybrid model. This model first obtains the entities identified by bidirectional long short-term memory and CNN, respectively, and then uses two hybrid strategies to output the final results relying on these entities. We also conduct experiments on raw medical records from real hospitals. This dataset is provided by the China Conference on Knowledge Graph and Semantic Computing in 2019 (CCKS 2019). Results demonstrate that the hybrid model can improve performance significantly.
WOS关键词INFORMATION
资助项目Major Special Project of Anhui Science and Technology Department[18030801133] ; Science and Technology Service Network Initiative[KFJ-STS-ZDTP-079]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000648719600017
出版者ASSOC COMPUTING MACHINERY
资助机构Major Special Project of Anhui Science and Technology Department ; Science and Technology Service Network Initiative
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/122254]  
专题中国科学院合肥物质科学研究院
通讯作者Sun, Yining
作者单位1.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, AnHui Prov Key Lab Med Phys & Technol, Hefei, Anhui, Peoples R China
2.Inst Intelligent Machines, Hefei, Anhui, Peoples R China
3.Univ Sci & Technol China, Hefei, Anhui, Peoples R China
4.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei, Anhui, Peoples R China
推荐引用方式
GB/T 7714
Wang, Yu,Sun, Yining,Ma, Zuchang,et al. A Hybrid Model for Named Entity Recognition on Chinese Electronic Medical Records[J]. ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING,2021,20.
APA Wang, Yu,Sun, Yining,Ma, Zuchang,Gao, Lisheng,&Xu, Yang.(2021).A Hybrid Model for Named Entity Recognition on Chinese Electronic Medical Records.ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING,20.
MLA Wang, Yu,et al."A Hybrid Model for Named Entity Recognition on Chinese Electronic Medical Records".ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 20(2021).

入库方式: OAI收割

来源:合肥物质科学研究院

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