WCP-RNN: a novel RNN-based approach for Bio-NER in Chinese EMRs: Paper ID: FC_17_25 - Li, Jianqiang; Zhao, Shenhe; Yang, Jijiang; Huang, Zhisheng; Liu, Bo; Chen, Shi; Pan, Hui; Wang, Qing (The Journal of supercomputing, 2020)
Deep learning has achieved remarkable success in a wide range of domains. However, it has not been comprehensively evaluated as a solution for the task of Chinese biomedical named entity recognition (Bio-NER). The traditional deep-learning approach for the Bio-NER task is usually based on the structure of recurrent neural networks (RNN) and only takes word embeddings into consideration, ignoring the value of character-level embeddings to encode the morphological and shape information. We propose an RNN-based approach, WCP-RNN, for the Chinese Bio-NER problem. Our method combines word embeddings and character embeddings to capture orthographic and lexicosemantic features. In addition, POS tags are involved as a priori word information to improve the final performance. The experimental results show our proposed approach outperforms the baseline method; the highest F -scores for subject and lesion detection tasks reach 90.36 and 90.48% with an increase of 3.10 and 2.60% compared with the baseline methods, respectively.