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Updated: Jun 1, 2025

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Read, Eliminate, and Focus: A reading comprehension paradigm for distant supervised relation extraction.

Zechen Meng1, Mankun Zhao1, Jian Yu1

  • 1College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China; Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin, 300350, China; Tianjin Key Laboratory of Advanced Networking, Tianjin, 300350, China.

Neural Networks : the Official Journal of the International Neural Network Society
|January 18, 2025
PubMed
Summary

This study introduces RelFoRE, a novel reading comprehension model for distant supervised relation extraction. RelFoRE effectively reduces noise and improves accuracy by simulating human reading comprehension to identify crucial entity pair information.

Keywords:
Distant supervisionKnowledge graphMachine reading comprehensionRelation extraction

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Information Extraction

Background:

  • Distant supervision for automatic machine annotation introduces significant noise in relation extraction.
  • Existing methods aggregate sentences into bags, losing contextual semantics and label information.
  • Attention mechanisms in current models struggle to fully mitigate the impact of noisy labels.

Purpose of the Study:

  • To develop a novel passage-level reading comprehension paradigm for distant supervised relation extraction.
  • To address the limitations of existing methods in handling noisy labels and preserving contextual semantics.
  • To improve the accuracy and robustness of relation extraction models.

Main Methods:

  • Proposed the RelFoRE (Relation Focused Reader) model, a passage-level reading comprehension approach.
  • Simulated human reading by eliminating incorrect options and focusing on crucial entity pair clues.
  • Implemented bidirectional interactions between passage, question, and options to extract key information.

Main Results:

  • RelFoRE demonstrated significant improvements over competing methods on three widely used datasets.
  • The model effectively reduced the influence of noisy labels by eliminating incorrect options.
  • Crucial entity pair information was successfully leveraged through the proposed reading comprehension paradigm.

Conclusions:

  • The RelFoRE model offers a promising new direction for distant supervised relation extraction.
  • Passage-level reading comprehension effectively tackles noise and enhances semantic understanding.
  • The proposed method significantly advances the state-of-the-art in automatic relation extraction.