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Related Experiment Video

Updated: Aug 24, 2025

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
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Relation Extraction in Biomedical Texts Based on Multi-Head Attention Model With Syntactic Dependency Feature:

Yongbin Li1, Linhu Hui1, Liping Zou1

  • 1School of Medical Information Engineering, Zunyi Medical University, Zunyi, China.

JMIR Medical Informatics
|October 20, 2022
PubMed
Summary

This study introduces a deep learning model for biomedical relation extraction, achieving state-of-the-art results on the SeeDev-binary task. The model effectively extracts relationships between entities in biomedical texts.

Keywords:
additive attentionbiomedical relation extractiondeep learningfeature combinationmulti-head attentionshortest dependency pathsyntactic dependency featuresyntactic dependency graph

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Area of Science:

  • Biomedical Natural Language Processing
  • Information Extraction
  • Computational Biology

Background:

  • Biomedical literature is rapidly expanding, increasing the need for automated information extraction.
  • Relation extraction between entities is a key challenge in processing this literature.

Purpose of the Study:

  • To perform two relation extraction tasks: Bacteria-Biotope (BB-rel) and plant seed development (SeeDev-binary).
  • To develop and evaluate a deep learning model for extracting relations between annotated entity pairs in biomedical texts.

Main Methods:

  • A deep learning model combining domain-specific word embeddings, POS embeddings, entity-type embeddings, distance embeddings, and position embeddings.
  • Utilized a multi-head attention mechanism for global semantic feature extraction.
  • Incorporated dependency-type features and shortest dependency paths from syntactic dependency graphs.

Main Results:

  • Achieved F1 scores of 65.56% on the BB-rel task and 38.04% on the SeeDev-binary task.
  • The model achieved state-of-the-art performance on the SeeDev-binary task, outperforming existing models.

Conclusions:

  • The multi-head attention mechanism effectively learns relevant syntactic and semantic features.
  • Syntactic dependency features enhance model performance by capturing entity relationships in biomedical texts.