WCP-RNN: a novel RNN-based approach for Bio-NER in Chinese EMRs Paper ID: FC_17_25

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 struct...

Full description

Saved in:
Bibliographic Details
Published inThe Journal of supercomputing Vol. 76; no. 3; pp. 1450 - 1467
Main Authors Li, Jianqiang, Zhao, Shenhe, Yang, Jijiang, Huang, Zhisheng, Liu, Bo, Chen, Shi, Pan, Hui, Wang, Qing
Format Journal Article
LanguageEnglish
Published New York Springer US 01.03.2020
Subjects
Compilers
Computer Science
Interpreters
Processor Architectures
Programming Languages
POS tags
Chinese EMRs
RNN-based model
Bio-NER
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-017-2229-x