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A co-adaptive duality-aware framework for biomedical relation extraction.

Weiyan Zhang1, Chuang Chen1, Jiacheng Wang1

  • 1School of Information Science and Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.

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This study introduces a new framework for biomedical relation extraction, improving the accuracy of identifying relationships in health records. The co-adaptive method enhances triplet extraction by considering entity and relation interactions.

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Area of Science:

  • Natural Language Processing
  • Biomedical Informatics

Background:

  • Biomedical relation extraction is crucial for mining electronic health records and building knowledge bases.
  • Existing methods often overlook the intricate interactions between entity pairs and relations within triplets.

Purpose of the Study:

  • To develop a novel framework for biomedical relation extraction that captures the rich interactions within triplet structures.
  • To address the limitations of previous pipeline and joint methods by focusing on the interdependence of entities and relations.

Main Methods:

  • Propose a co-adaptive biomedical relation extraction framework utilizing a duality-aware mechanism.
  • Implement a bidirectional extraction structure to account for the interdependence between subject-object entity pairs and relations.
  • Design co-adaptive training strategies and tuning algorithms for collaborative module optimization.

Main Results:

  • Achieved state-of-the-art F1 scores on two public datasets.
  • Demonstrated significant performance gains in complex scenarios, including overlapping patterns, multiple triplets, and cross-sentence triplets.
  • The proposed method effectively captures interactions within triplets for improved relation extraction.

Conclusions:

  • The novel co-adaptive framework enhances biomedical relation extraction by considering element interactions within triplets.
  • The duality-aware mechanism and collaborative optimization strategies lead to superior performance.
  • This approach offers a robust solution for complex biomedical text mining tasks.