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Yan Zhang1, Zhihao Yang1, Yumeng Yang1

  • 1School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.

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Summary
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This study introduces a novel attention generator for biomedical relation extraction, improving accuracy by comprehensively using syntactic and positional information to reduce noise and enhance text representation.

Keywords:
Biomedical relation extractionPosition informationSyntactic knowledge

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Area of Science:

  • Biomedical informatics
  • Natural Language Processing
  • Computational linguistics

Background:

  • Biomedical relation extraction is challenging due to complex texts.
  • Existing methods use syntactic knowledge but lack fine-grained noise reduction.
  • This can lead to confusion in relation classification.

Purpose of the Study:

  • To propose an attention generator for biomedical relation extraction.
  • To comprehensively utilize syntactic dependency type and position information.
  • To improve the accuracy of relation classification by reducing noise.

Main Methods:

  • Developed an attention generator considering syntactic dependency type and position.
  • Integrated positional, dependency type, and word representations.
  • Introduced location-enhanced syntactic knowledge for relation extraction.

Main Results:

  • The proposed approach consistently outperformed baseline models on three benchmark datasets.
  • Demonstrated effective utilization of syntactic knowledge.
  • Showcased significant reduction in the impact of noisy words.

Conclusions:

  • The novel attention generator effectively enhances biomedical relation extraction.
  • Integrating diverse syntactic and positional information improves model performance.
  • The method offers a robust solution for noise reduction in relation classification.