Fast and Accurate Recognition of Chinese Clinical Named Entities with Residual Dilated Convolutions
This addresses the need for faster and accurate CNER in electronic health records for clinical and translation research, but it is incremental as it replaces RNNs with a hybrid CNN-CRF approach.
The paper tackled the problem of slow training times in Chinese Clinical Named Entity Recognition (CNER) by proposing a Residual Dilated Convolutional Neural Network with Conditional Random Field (RD-CNN-CRF), which competes favorably with state-of-the-art RNN-based methods in computational performance and training time on the CCKS-2017 Task 2 benchmark dataset.
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 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 Conditional Random Field (RD-CNN-CRF) to solve it. Specifically, 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. 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.