Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehension.

Journal: Journal of biomedical informatics
PMID: 39490610

Abstract

OBJECTIVE: Although deep learning techniques have shown significant achievements, they frequently depend on extensive amounts of hand-labeled data and tend to perform inadequately in few-shot scenarios. The objective of this study is to devise a strategy that can improve the model's capability to recognize biomedical entities in scenarios of few-shot learning.

Authors

  • Leilei Su
    School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China.
  • Jian Chen
    School of Pharmacy, Shanghai Jiaotong University, Shanghai, China.
  • Yifan Peng
    Department of Population Health Sciences, Weill Cornell Medicine, New York, USA.
  • Cong Sun
    School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.

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