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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Few-shot biomedical named entity recognition via knowledge-guided instance generation and prompt contrastive

Peng Chen¹, Jian Wang¹, Hongfei Lin¹

¹School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.

Bioinformatics (Oxford, England)

|August 7, 2023

Summary

This summary is machine-generated.

Few-shot learning for biomedical named entity recognition (BioNER) is challenging due to limited data. This study introduces a novel knowledge-guided approach that significantly improves BioNER performance in low-resource settings.

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Area of Science:

Biomedical Informatics
Natural Language Processing
Machine Learning

Background:

Few-shot learning for biomedical named entity recognition (BioNER) is underexplored.
Low-resource scenarios in BioNER suffer from limited labeled data, hindering model generalization.
Existing methods face challenges with domain shift and low-quality synthetic data.

Purpose of the Study:

To address the challenges of few-shot learning in BioNER.
To develop a robust framework for BioNER in low-resource environments.
To improve the performance and generalization of BioNER models with minimal labeled data.

Main Methods:

Proposed a knowledge-guided instance generation method leveraging domain knowledge graphs.
Generated diverse and novel entities based on semantic relations of neighbor nodes.
Framed BioNER as a question-answering task using question prompts and introduced prompt contrastive learning.

Main Results:

The proposed framework achieved superior performance in various few-shot settings.
Demonstrated substantial improvement over state-of-the-art models in low-resource scenarios (20 samples).
Achieved an average improvement of up to 7.1% F1 score on four benchmark datasets.

Conclusions:

The knowledge-guided approach effectively enhances few-shot BioNER.
Prompt contrastive learning improves model robustness and performance.
The framework offers a promising solution for BioNER in data-scarce biomedical domains.