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

机构：[1] Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China; [2] Shanghai Hospital Development Center, Shanghai, China

年份：2018

外文期刊名：arXiv

收录：EI(收录号：20200349184)

语种：英文

外文关键词：Benchmarking - Clinical research - Deep neural networks - Large dataset - Natural language processing systems - Records management

摘要：Clinical Named Entity Recognition (CNER) 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 (NLP) 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 address the problem by incorporating dictionaries into deep neural networks for the Chinese CNER task. Two different architectures that extend the Bi-directional Long Short-Term Memory (Bi-LSTM) neural network and five different feature representation schemes are 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. Copyright ? 2018, The Authors. All rights reserved.