Data Augmentation for Chinese Clinical Named Entity Recognition

doi:10.13190/j.jbupt.2020-032

Journal of Beijing University of Posts and Telecommunications ›› 2020, Vol. 43 ›› Issue (5): 84-90.doi: 10.13190/j.jbupt.2020-032

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Data Augmentation for Chinese Clinical Named Entity Recognition

WANG Peng-hui, LI Ming-zheng, LI Si

School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2020-03-24 Published:2021-03-11

Abstract

Abstract: 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.

Key words: named entity recognition, data augmentation, generative adversarial network

CLC Number:

TP181

WANG Peng-hui, LI Ming-zheng, LI Si. Data Augmentation for Chinese Clinical Named Entity Recognition[J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(5): 84-90.

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