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Exploiting document graphs for inter sentence relation extraction.

Hoang-Quynh Le1, Duy-Cat Can2, Nigel Collier3

  • 1Faculty of Information Technology, VNU University of Engineering and Technology, Hanoi, Vietnam. lhquynh@vnu.edu.vn.

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Summary
This summary is machine-generated.

This study introduces a document subgraph representation to extract inter-sentence relations, improving relation extraction in biomedical texts. The novel approach enhances both precision and recall for these challenging cross-sentence facts.

Keywords:
Convolutional neural networkDeep learningGraphMultiple pathsRelation extraction

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Computational Biology

Background:

  • Traditional relation extraction (RE) primarily focuses on intra-sentence relationships.
  • Inter-sentence relations, connecting entities across sentences, remain underexplored despite their importance in document-level understanding.
  • Existing methods face challenges in capturing these document-level relational facts.

Purpose of the Study:

  • To develop a novel method for extracting inter-sentence relations.
  • To address the limitations of current relation extraction techniques in handling cross-sentence information.
  • To improve the accuracy and recall of relation extraction in biomedical literature.

Main Methods:

  • Introduction of a 'document subgraph' representation for sequences of sentences.
  • Application of deep learning models and graph-based techniques.
  • Evaluation on the BioCreative V Chemical-Disease Relation corpus.

Main Results:

  • The proposed system effectively extracts inter-sentence relations, achieving approximately 50% of these challenging relations.
  • Demonstrated significant improvements in both precision and recall compared to baseline models.
  • Validated the effectiveness of graph-based representations and deep learning components.

Conclusions:

  • The document subgraph representation is a robust approach for inter-sentence relation extraction.
  • This method enhances the capability to uncover relational facts spanning multiple sentences in biomedical texts.
  • The findings highlight the potential of graph-based deep learning for advanced information extraction.