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Knowledge-Driven Cross-Document Relation Extraction.

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This study introduces KXDocRE, a new method for cross-document relation extraction (CrossDocRE) that incorporates domain knowledge. This approach improves performance and provides interpretable relation predictions.

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Information Extraction

Background:

  • Relation Extraction (RE) is typically a sentence or document-level task.
  • Cross-document Relation Extraction (CrossDocRE) extends RE to multiple documents, posing unique challenges due to disparate themes.
  • Existing CrossDocRE methods overlook the crucial role of domain knowledge.

Purpose of the Study:

  • To propose KXDocRE, a novel framework for CrossDocRE that integrates domain knowledge.
  • To enhance the performance and interpretability of cross-document relation extraction.
  • To address the limitations of current CrossDocRE approaches by incorporating external knowledge.

Main Methods:

  • Developed KXDocRE, a framework that embeds entity domain knowledge directly with input text.
  • Utilized a novel approach to integrate structured domain knowledge into the CrossDocRE process.
  • Implemented methods for generating explanatory text to interpret predicted relations.

Main Results:

  • KXDocRE demonstrates improved performance compared to existing baseline methods in CrossDocRE.
  • The framework successfully incorporates domain knowledge, enhancing the accuracy of relation extraction.
  • The method provides interpretable outputs, explaining the basis for predicted relations.

Conclusions:

  • Integrating domain knowledge is crucial for advancing cross-document relation extraction.
  • KXDocRE offers a significant improvement in both performance and interpretability for CrossDocRE tasks.
  • The proposed framework paves the way for more sophisticated and knowledgeable information extraction systems.