A transition-based joint model for disease named entity recognition and normalization

Author:

Lou Yinxia12,Zhang Yue3,Qian Tao4,Li Fei1,Xiong Shufeng5,Ji Donghong1

Affiliation:

1. Computer School, Wuhan University, Wuhan, China

2. School of Computer and Information Technology, Shangqiu Normal University, Shangqiu, China

3. Singapore University of Technology and Design, Shangqiu Normal University, Shangqiu, China

4. College of Computer Science and Technology,Hubei University of Science and Technology, Xianning, China

5. Pingdingshan University, Pingdingshan, China

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference41 articles.

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1. Evaluation on Network Social Media Named Entity Recognition Model Based on Active Learning;ACM Transactions on Asian and Low-Resource Language Information Processing;2023-09-05

2. Improving biomedical named entity recognition through transfer learning and asymmetric tri-training;Procedia Computer Science;2023

3. Joint model of biomedical entity recognition and normalization labels based on self-attention;Second International Symposium on Computer Technology and Information Science (ISCTIS 2022);2022-12-08

4. TransCRF—Hybrid Approach for Adverse Event Extraction;Proceedings of Third Doctoral Symposium on Computational Intelligence;2022-11-10

5. A Review on the Application of Knowledge Graph Technology in the Medical Field;Scientific Programming;2022-07-20

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