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Related Experiment Video

Updated: Oct 13, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Utilizing Entity-Based Gated Convolution and Multilevel Sentence Attention to Improve Distantly Supervised Relation

Qian Yi1,2, Guixuan Zhang1,2, Shuwu Zhang1,2

  • 1Beijing Engineering Research Center of Digital Content Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100010, China.

Computational Intelligence and Neuroscience
|November 11, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel model for distantly supervised relation extraction (RE) that effectively handles noise in automatically collected datasets. The proposed method significantly improves RE performance by filtering noise and identifying valid sentences.

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Distant supervision is crucial for large-scale dataset creation in relation extraction (RE).
  • Automatically generated datasets often contain intrasentence noise and mislabeled sentences, hindering RE performance.
  • Existing methods struggle to effectively mitigate these noise types.

Purpose of the Study:

  • To propose a novel distantly supervised relation extraction model that addresses intrasentence noise and wrongly labeled sentences.
  • To enhance the feature extraction and sentence selection capabilities in distantly supervised RE.
  • To achieve state-of-the-art performance in relation extraction tasks.

Main Methods:

  • An entity-based gated convolution sentence encoder is employed to extract entity-pair-specific features and filter noise.
  • A multilevel sentence selective attention (Matt) module fuses bag information for fine-grained query vectors.
  • The Matt module aids in identifying valid sentences and reducing the impact of noisy data.

Main Results:

  • The proposed model effectively reduces the influence of both intrasentence noise and wrongly labeled sentences.
  • Experimental results on a large-scale benchmark dataset demonstrate superior performance.
  • The model achieves state-of-the-art results in distantly supervised relation extraction.

Conclusions:

  • The novel model successfully tackles noise challenges in distantly supervised relation extraction.
  • The entity-based gated convolution and multilevel attention mechanisms are key to the model's effectiveness.
  • This work advances the field of relation extraction by providing a robust solution for noisy datasets.