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Enhancing Biomedical Relation Extraction with Transformer Models using Shortest Dependency Path Features and Triplet

Vani Kanjirangat1, Fabio Rinaldi2

  • 1Istituto Dalle Molle di Studi sull'Intelligenza Artificiale USI/SUPSI, Lugano, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland.

Journal of Biomedical Informatics
|September 4, 2021
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Summary

This study introduces a novel method for biomedical relation extraction, using Shortest Dependency Path (SDP) features and triplet information with BioBERT to improve data sample selection and reduce noise in distantly supervised learning.

Keywords:
BioBERTInter-sententialIntra-sententialRelation extractionShortest dependency pathsTriplets

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Area of Science:

  • Biomedical informatics
  • Natural Language Processing
  • Machine Learning

Background:

  • Entity relation extraction is crucial for biomedical and clinical research.
  • Pre-trained transformer models show promise but face challenges with noisy data in distantly supervised methods.
  • The BioCreative V Chemical Disease Relation (CDR) dataset lacks mention-level annotations, necessitating effective sample selection.

Purpose of the Study:

  • To develop a novel approach for biomedical relation extraction, specifically addressing noisy data in distantly supervised settings.
  • To improve the selection of representative training samples by pruning noisy information.
  • To enhance model learning using Shortest Dependency Path (SDP) features and triplet information.

Main Methods:

  • Utilized Shortest Dependency Path (SDP) features to construct data samples and prune noisy information.
  • Employed triplet information in model learning with BioBERT, a biomedical variant of BERT.
  • Framed the problem as a sentence pair classification task, inputting sentence and entity-relation pairs.

Main Results:

  • The proposed approach demonstrated promising results, particularly for inter-sentential relation extraction on the CDR dataset.
  • The integration of SDP and triplet features effectively improved the selection of representative samples.
  • The method offers a computationally efficient alternative to considering entire documents or all sentences.

Conclusions:

  • The novel approach effectively handles noisy samples in distantly supervised biomedical relation extraction.
  • Shortest Dependency Path (SDP) features and triplet information significantly enhance model performance, especially for inter-sentential relations.
  • The publicly available code facilitates further research and application in the field.