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Related Experiment Video

Updated: Sep 24, 2025

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Distantly Supervised Relation Extraction via Recursive Hierarchy-Interactive Attention and Entity-Order Perception.

Ridong Han1, Tao Peng2, Jiayu Han3

  • 1College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 9, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Recursive Hierarchy-Interactive Attention network (RHIA) to improve distantly supervised relation extraction by considering entity order and relation hierarchy interactions. The RHIA-EOP model achieves state-of-the-art performance on the NYT dataset.

Keywords:
AttentionDistant SupervisionEntity OrderLong-tail RelationsRelation ExtractionRelation Hierarchies

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Distantly supervised relation extraction faces challenges from wrong labels and long-tail relations.
  • Existing methods often overlook entity appearance order and inter-level heuristic effects in relation hierarchies.

Purpose of the Study:

  • To propose a novel Recursive Hierarchy-Interactive Attention network (RHIA) that models heuristic effects between relation levels.
  • To introduce an Entity-Order Perception (EOP) training objective to capture entity appearance information.
  • To enhance the performance of distantly supervised relation extraction.

Main Methods:

  • Designed a Recursive Hierarchy-Interactive Attention network (RHIA) with a top-down information flow across relation levels.
  • Developed an Entity-Order Perception (EOP) training objective for sentence encoders.
  • Conducted experiments on the New York Times (NYT) dataset.

Main Results:

  • The proposed RHIA-EOP model achieved state-of-the-art performance.
  • Demonstrated significant improvements in precision-recall curves, AUC, and Top-N precision.
  • Experimental analysis confirmed the effectiveness of individual RHIA-EOP components.

Conclusions:

  • The RHIA-EOP model effectively addresses wrong-labeling and long-tail relation issues in distantly supervised relation extraction.
  • Incorporating entity order and hierarchical relation interactions significantly boosts model performance.
  • The proposed methods offer a promising direction for future relation extraction research.