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Leveraging shortest dependency paths in low-resource biomedical relation extraction.

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Shortest Dependency Paths (SDPs) improve biomedical relation extraction (RE) performance, especially with limited labeled data. This method enhances RE classifiers by leveraging sentence structure for better entity relationship identification.

Keywords:
BERTLow-resourceNatural language processingRelation extractionShortest dependency path

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Area of Science:

  • Biomedical informatics
  • Natural Language Processing

Background:

  • Biomedical Relation Extraction (RE) is crucial for identifying relationships between biomedical entities.
  • Training RE models is difficult in low-resource settings with scarce labeled data.

Purpose of the Study:

  • To investigate the utility of Shortest Dependency Paths (SDPs) for enhancing biomedical RE.
  • To explore methods for integrating SDPs into word and sentence representations across different learning paradigms.

Main Methods:

  • Utilized Shortest Dependency Paths (SDPs) to create feature representations.
  • Applied supervised, semi-supervised, and in-context learning approaches.
  • Experimented on three benchmark biomedical text datasets.

Main Results:

  • Incorporating SDP-based representations significantly improved RE classifier performance.
  • Performance gains were particularly pronounced in low-resource scenarios with minimal labeled data.

Conclusions:

  • SDPs provide valuable insights into the syntactic structure of biomedical text.
  • The proposed techniques effectively boost the accuracy of biomedical RE classifiers, especially under data scarcity.