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Document-level relation extraction with entity type constraints.

Ridong Han1, Tao Peng2, Beibei Zhu3

  • 1School of Computer Science and Technology/School of Artificial Intelligence, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China; College of Computer Science and Technology, Jilin University, Changchun, 130012, Jilin, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China.

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Summary
This summary is machine-generated.

This study introduces a novel method to improve document-level relation extraction by learning relation correlations using entity type constraints. This approach effectively addresses challenges like the long-tail problem and multi-label classification in relation extraction tasks.

Keywords:
Document-levelEntity type constraintsRelation extractionType-constrained graphType-constrained loss

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence

Background:

  • Document-level relation extraction faces challenges with long-tail and multi-label problems.
  • Existing methods using Transformers or document graphs struggle to address these issues effectively.

Purpose of the Study:

  • To propose a new method for learning relation correlations using entity type constraints to improve document-level relation extraction.
  • To address the limitations of current approaches in handling long-tail and multi-label relation extraction scenarios.

Main Methods:

  • Learning relation correlations from global and local views.
  • Constructing a Type-constrained Graph between entity types and relations for global correlation.
  • Proposing a Type-constrained Loss to enhance classification probabilities for matched relations locally.

Main Results:

  • The proposed model significantly outperforms existing baselines on the DocRED and DWIE datasets.
  • Demonstrated effectiveness in handling both long-tail and multi-label relation extraction setups.
  • Entity type constraints successfully guide the learning of relation correlations.

Conclusions:

  • Learning relation correlations via entity type constraints is a promising direction for document-level relation extraction.
  • The proposed method offers a robust solution for tackling long-tail and multi-label challenges.
  • This work advances the state-of-the-art in information extraction from documents.