An attention-based multi-task model for named entity recognition and intent analysis of Chinese online medical questions

被引：26

作者：

Wu, Chaochen ^{[1
]}

Luo, Guan ^{[1
]}

Guo, Chao ^{[2
]}

Ren, Yin ^{[3
]}

Zheng, Anni ^{[1
]}

Yang, Cheng ^{[1
]}

机构：

[1] Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China

[2] CAMS & PUMC, Dept Cardiol, Fuwai Hosp, Beijing 100037, Peoples R China

[3] Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China

来源：

JOURNAL OF BIOMEDICAL INFORMATICS | 2020年 / 108卷 / 108期

关键词：

Natural language processing; Deep learning; Multi-task learning; Named entity recognition; Intent analysis; Interpretability;

D O I：

10.1016/j.jbi.2020.103511

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this paper, we propose an attention-based multi-task neural network model for text classification and sequence tagging and then apply it to the named entity recognition and the intent analysis of Chinese online medical questions. We found that the use of both attention and multi-task learning improved the performance of these tasks. Our method achieved superior performance in named entity recognition and intent analysis compared with other baseline methods; the method is a light-weight solution that is suitable for deployment on small servers. Furthermore, we took advantage of the model's capabilities for these two tasks and built a simple question-answering system for cardiovascular issues. Users and service providers can monitor the logic of the answers generated by this system.

引用

页数：6

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