A weakly supervised Chinese medical named entity recognition method

被引：0

作者：

Zhao, Qing ^{[1
]}

Wang, Dan ^{[1
]}

Xu, Shushi ^{[1
]}

Zhang, Xiaotong ^{[2
]}

Wang, Xiaoxi ^{[3
]}

机构：

[1] Department of Information, Beijing University of Technology, Beijing,100124, China

[2] Binghamton University-SUNY, New York,13902, United States

[3] State Grid Management Institute, Beijing,102200, China

来源：

Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2020年 / 3卷 / 425-432期

关键词：

E-learning - Natural language processing systems - Embeddings - Semantics - Information retrieval - Ontology;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

To improve the accuracy of named entity recognition and reduce the cost of manual labeling, this study proposes a weakly supervised named entity recognition method based on the recurrent neural network (RNN), which utilizes the widely existing ontology in the medical field as the supplemental source of knowledge. In other words, a named entity recognition model is constructed by extracting semantic concept representation from medical ontology and integrating it with word and character embedding. First, the continuous bag of words model is utilized to extract semantic representation, including concept and word embedding. Then, the character-enhanced word embedding model is used to extract character representation. Finally, the tag sequence of Chinese medical text is obtained using a deep learning model RNN in combination with semantic and character embedding. The results of a comparative experiment on a true dataset of medical text show that the performance improvement of our proposed method compared with that of traditional methods reaches 2.2% to 6.1%, which verifies the effectiveness of our proposed method. © 2020, Editorial Department of Journal of HEU. All right reserved.

引用

页码：425 / 432