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Multidimensional information-guided mention integration and multidimensional reasoning for biomedical document-level

Xinyu He1, Yuning Zhang2, Yonggong Ren2

  • 1School of Computer and Artificial Intelligence, Liaoning Normal University, Dalian, Liaoning, China; Information and Communication Engineering Postdoctoral Research Station, Dalian University of Technology, Dalian, Liaoning, China; Postdoctoral Workstation of Dalian Yongjia Electronic Technology Co., Ltd, Dalian, Liaoning, China.

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|December 23, 2025
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Summary
This summary is machine-generated.

This study introduces novel methods for biomedical document-level relation extraction, improving the identification of complex relationships within texts. The approach enhances entity representations and infers implicit connections for better pathway reconstruction.

Keywords:
Biomedical document-level relation extractionChemical-disease relation extractionMention integrationReasoningU-Net

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Area of Science:

  • Biomedical informatics
  • Natural Language Processing
  • Computational Biology

Background:

  • Biomedical document-level relation extraction is challenging due to multi-sentence entity mentions.
  • Existing methods often overlook mention-level details and use simple pooling for entity representation.
  • Irrelevant mentions and noisy data complicate accurate relationship identification.

Purpose of the Study:

  • To develop a robust method for biomedical document-level relation extraction.
  • To improve entity representations by effectively integrating mention-level information.
  • To infer implicit and complex relationships within biomedical texts.

Main Methods:

  • Introduced a Multidimensional Information-Guided Mention Integration module with mention graphs and context-aware attention.
  • Developed a Multidimensional Reasoning module using logical patterns and a U-Net network for inferring implicit relationships.
  • Focused on filtering irrelevant mentions and capturing context-sensitive information.

Main Results:

  • Achieved high F-scores of 86.7% on the CDR dataset and 84.7% on the GDA dataset.
  • Demonstrated robust entity representations by filtering noisy mentions.
  • Successfully reconstructed complex biomedical pathways and entity interactions.

Conclusions:

  • The proposed method significantly enhances biomedical document-level relation extraction.
  • Effective integration of mention-level information and multidimensional reasoning improves accuracy.
  • The approach shows strong performance on both biomedical and general-domain datasets.