Enhancing biomedical named entity recognition with parallel boundary detection and category classification.

Journal: BMC bioinformatics
PMID: 40000968

Abstract

BACKGROUND: Named entity recognition is a fundamental task in natural language processing. Recognizing entities in biomedical text, known as the BioNER, is particularly crucial for cutting-edge applications. However, BioNER poses greater challenges compared to traditional NER due to (1) nested structures and (2) category correlations inherent in biomedical entities. Recently, various BioNER models have been developed based on region classification or large language models. Despite being successful, these models still struggle to balance handling nested structures and capturing category knowledge.

Authors

  • Yu Wang
    Clinical and Technical Support, Philips Healthcare, Shanghai, China.
  • Hanghang Tong
    Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
  • Ziye Zhu
    Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing, China.
  • Fengzhen Hou
    Key Laboratory of Biomedical Functional Materials, School of Science, China Pharmaceutical University, Nanjing 210009, China. Electronic address: houfz@cpu.edu.cn.
  • Yun Li
    School of Public Health, University of Michigan, Ann Arbor, MI, USA.

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