Data Augmentation for Chinese Clinical Named Entity Recognition

被引：0
|
作者：
Wang P.-H. [1 ]
Li M.-Z. [1 ]
Li S. [1 ]
机构：
[1] School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing
来源：
Li, Si (lisi@bupt.edu.cn) | 1600年 / Beijing University of Posts and Telecommunications卷 / 43期
关键词：
Data augmentation; Generative adversarial network; Named entity recognition;
D O I：
10.13190/j.jbupt.2020-032
中图分类号：
学科分类号：
摘要：
Chinese clinical named entity recognition plays an important role in recognizing medical entities contained in Chinese electronic medical records. Limited to lack of large annotated data, most of existing methods concentrate on employing external resources to improve the performance of clinical named entity recognition, which require lots of time and efficient rules. To solve the problem of lack of large annotated data, data augmentation using sequence adversarial generative network is used to generate more various data depending on entities and non-entities in the training set. Experiments show that when using generated data to expand training set, the proposed named entity recognition system has achieved competitive performance compared with state-of-art methods, which shows the effectiveness of our data augmentation method. © 2020, Editorial Department of Journal of Beijing University of Posts and Telecommunications. All right reserved.
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收藏
页码：84 / 90
页数：6
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