文献类型：期刊文献

英文题名：Chinese Clinical Named Entity Recognition Using Residual Dilated Convolutional Neural Network With Conditional Random Field

作者：Qiu, Jiahui[1];Zhou, Yangming[1];Wang, Qi[1];Ruan, Tong[1];Gao, Ju[2]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]Shanghai Shuguang Hosp, Shanghai 200120, Peoples R China

年份：2019

卷号：18

期号：3

起止页码：306

外文期刊名：IEEE TRANSACTIONS ON NANOBIOSCIENCE

收录：;EI(收录号：20191506748729);WOS:【SCI-EXPANDED(收录号:WOS:000473625900005)】；

基金：This work was supported in part by Shanghai Sailing Program under Grant 19YF1412400, National Key Research and Development Program of China for Precision Medical Research under Grant 2018YFC0910500, National Natural Science Foundation of China under Grant 61772201, and National Major Scientific and Technological Special Project for Significant New Drugs Development under Grant 2018ZX09201008.

语种：英文

外文关键词：Electronic health records; clinical named entity recognition; residual dilated convolutional neural network; conditional random field

摘要：Clinical named entity recognition (CNER) is a fundamental and crucial task for clinical and translation research. In recent years, deep learning methods have achieved significant success in CNER tasks. However, these methods depend greatly on recurrent neural networks (RNNs), which maintain a vector of hidden activations that are propagated through time, thus causing too much time to train models. In this paper, we propose a residual dilated convolutional neural network with the conditional random field (RD-CNN-CRF) for the Chinese CNER, which makes the model asynchronous in computation and thus speeding up the training period dramatically. To be more specific, Chinese characters and dictionary features are first projected into dense vector representations, then they are fed into the residual dilated convolutional neural network to capture contextual features. Finally, a conditional random field is employed to capture dependencies between neighboring tags and obtain the optimal tag sequence for the entire sequence. Computational results on the CCKS-2017 Task 2 benchmark dataset show that our proposed RD-CNN-CRF method competes favorably with state-of-the-art RNN-based methods both in terms of computational performance and training time.