Entity recognition in Chinese clinical text using attention-based CNN-LSTM-CRF

BMC Medical Informatics and Decision Making

Xiaolong Wang¹  Qingcai Chen¹  Buzhou Tang¹  Jun Yan² 

[1] Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology, (Shenzhen);Yidu Cloud (Beijing) Technology Co., Ltd;

关键词: Chinese clinical entity recognition;    Neural network;    Convolutional neural network;    Long-short term memory;    Conditional random field;   

DOI  :  10.1186/s12911-019-0787-y

来源: DOAJ

【 摘 要 】

Abstract Background Clinical entity recognition as a fundamental task of clinical text processing has been attracted a great deal of attention during the last decade. However, most studies focus on clinical text in English rather than other languages. Recently, a few researchers have began to study entity recognition in Chinese clinical text. Methods In this paper, a novel deep neural network, called attention-based CNN-LSTM-CRF, is proposed to recognize entities in Chinese clinical text. Attention-based CNN-LSTM-CRF is an extension of LSTM-CRF by introducing a CNN (convolutional neural network) layer after the input layer to capture local context information of words of interest and an attention layer before the CRF layer to select relevant words in the same sentence. Results In order to evaluate the proposed method, we compare it with other two currently popular methods, CRF (conditional random field) and LSTM-CRF, on two benchmark datasets. One of the datasets is publically available and only contains contiguous clinical entities, and the other one is constructed by us and contains contiguous and discontiguous clinical entities. Experimental results show that attention-based CNN-LSTM-CRF outperforms CRF and LSTM-CRF. Conclusions CNN and attention mechanism are individually beneficial to LSTM-CRF-based Chinese clinical entity recognition system, no matter whether contiguous clinical entities are considered. The conribution of attention mechanism is greater than CNN.

【 授权许可】

Unknown