Ontology Attention Layer for Medical Named Entity Recognition

Author:

Zha Yue1,Ke Yuanzhi1ORCID,Hu Xiao1,Xiong Caiquan1

Affiliation:

1. School of Computer Science, Hubei University of Technology, Wuhan 430068, China

Abstract

Named entity recognition (NER) is particularly challenging for medical texts due to the high domain specificity, abundance of technical terms, and sparsity of data in this field. In this work, we propose a novel attention layer, called the “ontology attention layer”, that enhances the NER performance of a language model for clinical text by utilizing an ontology consisting of conceptual classes related to the target entity set. The proposed layer computes the relevance between each input token and the classes in the ontology and then fuses the encoded token vectors and the class vectors to enhance the token vectors by explicit superior knowledge. In our experiments, we apply the proposed layer to various language models for an NER task based on a Chinese clinical dataset to evaluate the performance of the layer. We also investigate the influence of the granularity of the classes utilized in the ontology attention layer. The experimental results show that the proposed ontology attention layer improved F1 scores by 0.4% to 0.5%. The results suggest that the proposed method is an effective approach to improving the NER performance of existing language models for clinical datasets.

Funder

National College Students Innovation and Entrepreneurship Training Program, China

Publisher

MDPI AG

Reference34 articles.

1. Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., and Dyer, C. (2016, January 12–17). Neural Architectures for Named Entity Recognition. Proceedings of the North American Chapter of the Association for Computational Linguistics, San Diego, CA, USA.

2. Named Entity Recognition of Medical Text Based on the Deep Neural Network;Yang;J. Healthc. Eng.,2022

3. Named Entity Recognition in Chinese Electronic Medical Records Based on the Model of Bidirectional Long Short-Term Memory with a Conditional Random Field Layer;Li;Stud. Health Technol. Inform.,2019

4. Wang, S., Sun, X., Li, X., Ouyang, R., Wu, F., Zhang, T., Li, J., and Wang, G. (2023). GPT-NER: Named Entity Recognition via Large Language Models. arXiv.

5. Jiang, H., Zhang, D., Cao, T., Yin, B., and Zhao, T. (2021, January 22–27). Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data. Proceedings of the Annual Meeting of the Association for Computational Linguistics, Bangkok, Thailand.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3