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A Syntax-enhanced model based on category keywords for biomedical relation extraction.

Xiaofeng Liu1, Jiajie Tan2, Jianye Fan2

  • 1School of Computer Science and Engineering, South China University of Technology, Guangzhou, China; Zhongshan Institute of Modern Industrial Technology, South China University of Technology, Zhongshan, China.

Journal of Biomedical Informatics
|July 16, 2022
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Summary
This summary is machine-generated.

This study introduces a new syntax-enhanced model for biomedical relationship extraction. The model improves accuracy by using category keywords and pruned dependency trees to better capture linguistic features.

Keywords:
Category keywordsMulti-category biomedical relation extractionSyntactic dependency treeSyntax-enhanced model

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Area of Science:

  • Biomedical Natural Language Processing
  • Computational Linguistics
  • Bioinformatics

Background:

  • Multi-category biomedical relationship extraction faces challenges due to linguistic similarities between categories.
  • Existing pre-trained models struggle to accurately mine nuanced information from these similar categories.
  • Syntactic structures and category-specific keywords offer valuable, yet underutilized, features for improving extraction accuracy.

Purpose of the Study:

  • To develop a novel syntax-enhanced model for multi-category biomedical relationship extraction.
  • To leverage category keywords and pruned syntactic dependency trees to improve model performance.
  • To enhance the pre-trained model's ability to capture complex syntax structures and differentiate between similar biomedical categories.

Main Methods:

  • Pruning syntactic dependency trees based on category keywords identified via chi-square test to reduce noise.
  • Developing a syntactic transformer to encode category-related syntactic dependency trees.
  • Integrating the enhanced syntactic information into a pre-trained model for biomedical relationship extraction.

Main Results:

  • The proposed syntax-enhanced model demonstrated superior performance compared to state-of-the-art models on three benchmark biomedical datasets.
  • The method effectively reduced noisy information while retaining category-relevant syntactic features.
  • Further analysis confirmed the model's enhanced capability in capturing syntax structures and distinguishing between multiple biomedical relationship categories.

Conclusions:

  • The syntax-enhanced model offers a significant advancement in multi-category biomedical relationship extraction.
  • The approach of pruning dependency trees and using a syntactic transformer effectively addresses limitations of current pre-trained models.
  • This method provides a more accurate and robust solution for extracting complex relationships from biomedical texts.