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Related Experiment Video

Updated: May 26, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

DA-BioNER: data augmentation based on few-shot learning and distant supervision for biomedical named entity

Yesol Park1, Gyujin Son2, Taeuk Kim1,2

  • 1Department of Computer Science, Hanyang University, Seoul, Republic of Korea.

Bioinformatics (Oxford, England)
|May 24, 2026
PubMed
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This summary is machine-generated.

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This study introduces DA-BioNER, a novel framework for biomedical named entity recognition (NER). DA-BioNER enhances low-resource NER by refining existing annotations with large language models, improving accuracy and entity diversity.

Area of Science:

  • Biomedical informatics
  • Natural Language Processing
  • Machine Learning

Background:

  • Named Entity Recognition (NER) is crucial for structured knowledge extraction in biomedicine.
  • Scarcity of high-quality annotated data hinders NER effectiveness in emerging biomedical domains.
  • Existing data augmentation methods often suffer from limited entity diversity, noisy labels, and disrupted contextual integrity.

Purpose of the Study:

  • To develop a context-preserving data expansion framework for biomedical NER.
  • To address the challenges of low-resource settings and domain adaptation in biomedical NER.
  • To improve the generalization ability of NER models in data-scarce environments.

Main Methods:

  • DA-BioNER framework combines multiple base NER models for coarse annotations.

Related Experiment Videos

Last Updated: May 26, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

  • A large language model (LLM) refines annotations using global biomedical knowledge.
  • Annotation refinement occurs within existing sentences, preserving syntactic and semantic context.
  • LLM's role is constrained to refinement, reducing hallucination and improving precision.

    • Main Results:

    • DA-BioNER achieves high F1-scores on benchmark datasets (NCBI-Disease, BC5CDR, BioRED) in low-resource settings (e.g., 0.799 on BioRED in 40-shot).
    • Outperforms state-of-the-art methods by up to 0.32 in 40-shot settings.
    • Improves F1-scores by up to 0.08 in extreme few-shot settings.
    • Generates an average of 1,391 additional unique entities, enhancing training diversity.

    Conclusions:

    • DA-BioNER offers a scalable and adaptable solution for robust biomedical NER.
    • The framework is particularly effective for domain adaptation and low-resource scenarios.
    • DA-BioNER preserves contextual integrity while significantly improving NER performance and data diversity.