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Updated: Jul 8, 2025
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RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
Published on: July 17, 2021
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Document-level Relation Extraction with Relation Correlations
Ridong Han1, Tao Peng1, Benyou Wang2
1College of Computer Science and Technology, Jilin University, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, China.
Summary
This study introduces relation co-occurrence correlations to improve document-level relation extraction, effectively addressing long-tail and multi-label challenges for better knowledge transfer and relation identification.
Area of Science:
- Natural Language Processing
- Artificial Intelligence
- Machine Learning
Background:
- Document-level relation extraction faces challenges with long-tail and multi-label data.
- Existing methods primarily focus on entity pair representations, neglecting these specific issues.
Purpose of the Study:
- To introduce relation co-occurrence correlations into document-level relation extraction.
- To leverage these correlations for knowledge transfer and improved multi-label classification.
Main Methods:
- Analyzing and incorporating co-occurrence correlations of relations.
- Utilizing relation embeddings and proposing two co-occurrence prediction sub-tasks (coarse- and fine-grained).
- Employing learned correlation-aware embeddings to guide relational fact extraction.
Main Results:
- Achieved superior performance on DocRED and DWIE datasets compared to baseline methods.
- Demonstrated the effectiveness of relation correlations in addressing long-tail and multi-label problems.
- Validated through substantial experiments and insightful analysis.
Conclusions:
- Relation co-occurrence correlations offer a promising approach to enhance document-level relation extraction.
- The proposed method effectively tackles data scarcity and improves the identification of semantically related relations.
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