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Integrating graph convolutional networks to enhance prompt learning for biomedical relation extraction.

Bocheng Guo1, Jiana Meng1, Di Zhao2

  • 1School of Computer Science and Engineering, Dalian Minzu University, Dalian, 116650, Liaoning, China.

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|August 29, 2024
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Summary

This study introduces a novel biomedical relation extraction model that integrates Graph Convolutional Networks (GCNs) with prompt learning. This approach effectively captures syntactic dependencies, significantly improving relation extraction performance in medical texts.

Keywords:
Graph convolutional networksPre-trained language modelsPrompt learningRelation extraction

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Area of Science:

  • Natural Language Processing
  • Bioinformatics
  • Computational Linguistics

Background:

  • Biomedical relation extraction identifies relationships between entities in medical texts.
  • Current models often struggle with grammatical dependencies, limiting their effectiveness.
  • Graph Convolutional Networks (GCNs) show promise in processing syntactic information.

Purpose of the Study:

  • To develop an improved biomedical relation extraction model.
  • To address limitations in handling syntactic dependencies in existing models.
  • To enhance performance by integrating GCNs with prompt learning.

Main Methods:

  • A novel model fusing GCNs-enhanced prompt learning for relation extraction.
  • Integration of syntactic dependency information analyzed by GCNs into a prompt learning framework.
  • Predicting [MASK] token labels for relation extraction based on integrated syntactic information.

Main Results:

  • The proposed model achieved high F1 scores across multiple biomedical datasets: GAD (85.57%), ChemProt (80.15%), PGR (95.10%), and DDI (84.11%).
  • Performance surpassed existing baseline models on these datasets.
  • Demonstrated the effectiveness of integrating GCNs for syntactic dependency analysis.

Conclusions:

  • Enhancing prompt learning with GCNs effectively incorporates syntactic information into biomedical relation extraction.
  • The proposed method achieves excellent performance in biomedical relation extraction tasks.
  • The integration of GCNs offers a promising direction for future relation extraction research.