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Document-Level Biomedical Relation Extraction Leveraging Pretrained Self-Attention Structure and Entity Replacement:

Xiaofeng Liu1, Jianye Fan1, Shoubin Dong1

  • 1Communication and Computer Network Key Laboratory of Guangdong, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.

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Summary
This summary is machine-generated.

This study introduces a novel approach for document-level biomedical relation extraction, improving precision and F1 scores by utilizing a pretrained self-attention structure and an entity replacement method to handle long-distance dependencies and complex semantics effectively.

Keywords:
biomedical entity pretreatmentdocument-levelrelation extractionself-attention

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Area of Science:

  • Biomedical informatics
  • Natural Language Processing
  • Computational Biology

Background:

  • Current intrasentence relation extraction methods are insufficient for document-level tasks where relationships span sentences.
  • Existing document-level approaches often introduce noise by splitting datasets and fail to adequately address intersentence relation extraction.
  • Extracting cross-sentence relations in biomedical literature presents significant challenges due to noise and complexity.

Purpose of the Study:

  • To develop a method that avoids errors associated with dataset splitting in document-level relation extraction.
  • To evaluate the efficacy of a self-attention structure in capturing long-distance dependencies and complex semantics for biomedical relation extraction.
  • To compare the performance of various biomedical entity pretreatment methods.

Main Methods:

  • A new data preprocessing technique was developed for document-level biomedical relation extraction.
  • A pretrained self-attention structure was applied, incorporating an entity replacement strategy.
  • This approach aims to effectively capture very long-distance dependencies and intricate semantic relationships within documents.

Main Results:

  • The proposed method demonstrated significant improvements in precision compared to state-of-the-art techniques.
  • The approach led to an increase in the F1 score, outperforming existing methods.
  • Experiments confirmed that employing an entity replacement method enhances model performance in biomedical relation extraction.

Conclusions:

  • A pretrained self-attention structure is effective for document-level biomedical relation extraction, capturing long-range dependencies and complex semantics.
  • Treating target entity pairs holistically within the document-level dataset is beneficial.
  • Biomedical entity replacement is a valuable strategy, particularly enhancing document-level relation extraction performance.