文献类型：期刊文献

英文题名：Incorporating dictionaries into deep neural networks for the Chinese clinical named entity recognition

作者：Wang, Qi[1];Zhou, Yangming[1];Ruan, Tong[1];Gao, Daqi[1];Xia, Yuhang[1];He, Ping[2]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]Shanghai Hosp, Dev Ctr, Shanghai 200040, Peoples R China

年份：2019

卷号：92

外文期刊名：JOURNAL OF BIOMEDICAL INFORMATICS

收录：;EI(收录号：20191006593002);WOS:【SSCI(收录号:WOS:000525688900006)，SCI-EXPANDED(收录号:WOS:000525688900006)】；

基金：We would like to thank the reviewers for their useful comments and suggestions. This work was supported by the National Key R&D Program of China for "Precision Medical Research" (No. 2018YFC0910500), National Major Scientific and Technological Special Project for "Significant New Drugs Development" (No. 2018ZX09201008), and the National Natural Science Foundation of China (No. 61772201).

语种：英文

外文关键词：Clinical named entity recognition; Electronic health records; Deep neural network; Dictionary features

摘要：Clinical named entity recognition aims to identify and classify clinical terms such as diseases, symptoms, treatments, exams, and body parts in electronic health records, which is a fundamental and crucial task for clinical and translational research. In recent years, deep neural networks have achieved significant success in named entity recognition and many other natural language processing tasks. Most of these algorithms are trained end to end, and can automatically learn features from large scale labeled datasets. However, these data-driven methods typically lack the capability of processing rare or unseen entities. Previous statistical methods and feature engineering practice have demonstrated that human knowledge can provide valuable information for handling rare and unseen cases. In this paper, we propose a new model which combines data-driven deep learning approaches and knowledge-driven dictionary approaches. Specifically, we incorporate dictionaries into deep neural networks. In addition, two different architectures that extend the bi-directional long short-term memory neural network and five different feature representation schemes are also proposed to handle the task. Computational results on the CCKS-2017 Task 2 benchmark dataset show that the proposed method achieves the highly competitive performance compared with the state-of-the-art deep learning methods.