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Chinese medical named entity recognition integrating adversarial training and feature enhancement

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成果类型:
期刊论文
作者:
Zhang, Xu;Kao, Youchen;Che, Shengbing;Yan, Juan;Zhou, Sha;...
通讯作者:
Che, SB
作者机构:
[Wang, Wanqin; Che, SB; Che, Shengbing; Guo, Shenyi; Zhang, Xu; Kao, Youchen] Cent South Univ Forestry & Technol, Coll Comp Sci & Math, 498 Shaoshan South Rd,Wenyuan St, Changsha 410004, Hunan, Peoples R China.
[Kao, Youchen] Cent South Univ Forestry & Technol, Informat & Engn Coll, Swan Coll, 1-10 Furong North Rd, Changsha 410211, Hunan, Peoples R China.
[Zhou, Sha] Cent South Univ Forestry & Technol, Coll Elect Informat & Phys, 498 Shaoshan South Rd,Wenyuan St, Changsha 410004, Hunan, Peoples R China.
[Yan, Juan] Xiaoxian Hosp, Affiliated Hosp 1, Bengbu Med Coll, Dept Gastroenterol, Suzhou 235299, Anhui, Peoples R China.
通讯机构:
[Che, SB ] C
Cent South Univ Forestry & Technol, Coll Comp Sci & Math, 498 Shaoshan South Rd,Wenyuan St, Changsha 410004, Hunan, Peoples R China.
语种:
英文
关键词:
Entity extraction;Data enhancement;BPBIC model;Adversarial training;Knowledge graph
期刊:
Scientific Reports
ISSN:
2045-2322
年:
2025
卷:
15
期:
1
页码:
14844
基金类别:
TAPU Research Fund; [2024-1128-01-HNTP01]
机构署名:
本校为第一且通讯机构
院系归属:
涉外学院
摘要:
Chinese possesses the essential attributes of unique character composition structure and the nested nature of medical entities, which causes many challenges for Chinese Electronic Health Records (EHRs) in medical named entity recognition tasks, such as scarce annotated data, strong tokenization ambiguity, and blurred entity boundaries. This increases the difficulty of extracting medical named entity categories. The paper proposes an effective Chinese clinical named entity recognition model that integrates BERT and adversarial enhancement in a dual channel architecture to address this issue. Fi...

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