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Learning entity-oriented representation for biomedical relation extraction.

Ying Hu1, Yanping Chen1, Yongbin Qin1

  • 1Text Computing and Cognitive Intelligence Engineering Research Center of National Education Ministry, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.

Journal of Biomedical Informatics
|October 18, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an Entity-oriented Representation (EoR) model to improve biomedical relation extraction by reducing semantic overlap. The EoR model enhances distinctiveness between entity pairs, achieving state-of-the-art results across multiple tasks.

Keywords:
Biomedical natural language processingInformation extractionOverlapping semantics

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Natural Language Processing

Background:

  • Biomedical Relation Extraction (BioRE) is crucial for biomedical research.
  • Existing methods struggle with overlapping semantics in sentences with multiple entity pairs.
  • Overlapping semantics leads to similar information for different entity pairs, hindering accuracy.

Purpose of the Study:

  • To propose a novel model for learning Entity-oriented Representation (EoR) to enhance BioRE performance.
  • To improve the discriminability between entity pairs sharing the same context.
  • To address the challenge of overlapping semantics in BioRE.

Main Methods:

  • Developed a three-module model: sentence representation, entity-oriented representation, and output.
  • The entity-oriented representation module focuses on semantic information relative to target entities.
  • The output module enhances distinguishability among entity pairs for relation classification.

Main Results:

  • The EoR model achieved state-of-the-art performance on four BioRE tasks: Protein-Protein Interaction (PPI), Drug-Drug Interaction (DDI), Chemical-Protein Interaction (CPI), and Disease-Phenotype Interaction (DPI).
  • Experiments were conducted on eight diverse BioRE datasets.
  • Further analysis confirmed the benefits of entity-oriented semantic information in handling complex sentences with multiple entity pairs.

Conclusions:

  • The proposed Entity-oriented Representation (EoR) model significantly improves biomedical relation extraction.
  • EoR effectively enhances the discriminability of entity pairs, overcoming limitations of overlapping semantics.
  • This approach offers a robust solution for accurate relation extraction in complex biomedical texts.