Biomedical Named Entity Recognition Based on Self-supervised Deep Belief Network

ZHANG Yajun; LIU Zongtian; ZHOU Wen

doi:10.1049/cje.2020.03.001

Volume 29 Issue 3

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ZHANG Yajun, LIU Zongtian, ZHOU Wen, “Biomedical Named Entity Recognition Based on Self-supervised Deep Belief Network,” Chinese Journal of Electronics, vol. 29, no. 3, pp. 455-462, 2020, doi: 10.1049/cje.2020.03.001

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ZHANG Yajun, LIU Zongtian, ZHOU Wen, “Biomedical Named Entity Recognition Based on Self-supervised Deep Belief Network,” Chinese Journal of Electronics, vol. 29, no. 3, pp. 455-462, 2020, doi: 10.1049/cje.2020.03.001

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Biomedical Named Entity Recognition Based on Self-supervised Deep Belief Network

doi: 10.1049/cje.2020.03.001

1.
Shanghai Institute of Precision Measurement and Test, Shanghai 201109, China;
2.
School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61305053, No.61273328, No.71203135).

Received Date: 2018-10-08
Rev Recd Date: 2019-01-02
Publish Date: 2020-05-10

Abstract

Abstract

Named entity recognition is a fundamental and crucial issue of biomedical data mining. For effectively solving this issue, we propose a novel approach based on Deep belief network (DBN). We select nine entity features, and construct feature vector mapping tables by the recognition contribution degree of different values of them. Using the mapping tables, we transform words in biomedical texts to feature vectors. The DBN will identify entities by reducing dimensions of vector data. The extensive experimental results reveal that the novel approach has achieved excellent recognition performance, with 69.96% maximum value of F-measure on GENIA 3.02 testing corpus. We propose a self-supervised DBN, which can decide whether to add supervised fine-tuning or not according to the recognition performance of each layer, can overcome the errors propagation problem, while the complexity of model is limited. Test analysis shows that the new DBN improves recognition performance, the Fmeasure increases to 72.12%.
- Biomedical named entity recognition,
- Feature selection,
- Feature vector mapping,
- Threshold judgement,
- Self-supervision

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