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 |
| DOI | 10.1145/3436819 |
| 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
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。