8 research outputs found
Fast and Accurate Recognition of Chinese Clinical Named Entities with Residual Dilated Convolutions
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.Comment: 8 pages, 3 figures. Accepted as regular paper by 2018 IEEE
International Conference on Bioinformatics and Biomedicine. arXiv admin note:
text overlap with arXiv:1804.0501