A Survey on Deep Learning for Chinese Medical Named Entity Recognition

Author:

Dai Chenquan1ORCID,Zhuang Xiaobin2ORCID,Cai Jiaxin2ORCID

Affiliation:

1. School of Computer and Information Engineering, Xiamen University of Technology, China

2. School of Mathematics and Statistics, Xiamen University of Technology, China

Funder

China University Industry University Research Innovation Fund

Fujian Province Young and Middle-aged Teacher Education Research Project

Fujian Province Social Science Foundation Project

Natural Science Foundation of Fujian Province

Publisher

ACM

Reference32 articles.

1. National Health and Family Planning Commission of the People's Republic of China. ( 2010 ) Notice of the Ministry of Health on the issuance of the Basic Specifications for Electronic Medical Records (Trial) [EB/OL] . http://www.gov.cn/zwgk/2010-03/04/content_1547432.htm National Health and Family Planning Commission of the People's Republic of China. (2010) Notice of the Ministry of Health on the issuance of the Basic Specifications for Electronic Medical Records (Trial) [EB/OL]. http://www.gov.cn/zwgk/2010-03/04/content_1547432.htm

2. A comprehensive study of named entity recognition in Chinese clinical text

3. Message Understanding Conference-6

4. Ling Luo.(2021) Performance of existing methods for entity recognition Chinese electronic medical records [EB/OL]. https://github.com/lingluodlut/Chinese-BioNLP/blob/main/CNER_sota.md#17 Ling Luo.(2021) Performance of existing methods for entity recognition Chinese electronic medical records [EB/OL]. https://github.com/lingluodlut/Chinese-BioNLP/blob/main/CNER_sota.md#17

5. Mikolov T , Sutskever I , Chen K , ( 2013 ). Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems 26. 2–3 . Mikolov T, Sutskever I, Chen K, (2013). Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems 26. 2–3.

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