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Distant supervision for relation extraction with hierarchical selective attention.

Peng Zhou1, Jiaming Xu2, Zhenyu Qi2

  • 1Institute of Automation, Chinese Academy of Sciences (CAS), China; University of Chinese Academy of Sciences (UCAS), China.

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Summary
This summary is machine-generated.

This study introduces a new hierarchical selective attention network for distant supervised relation extraction. The model efficiently identifies key sentences, improving accuracy and reducing computational cost in natural language processing.

Keywords:
Distant supervisionHierarchical attentionPiecewise convolutional neural networksRelation extraction

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Distant supervised relation extraction is crucial for information retrieval.
  • Current methods face challenges with high computational costs and identifying relevant sentences.
  • Inefficient processing of all sentences for an entity pair hinders performance.

Purpose of the Study:

  • To propose a novel hierarchical selective attention network for relation extraction.
  • To address the computational cost and relevance identification issues in distant supervision.
  • To enhance the accuracy of relation extraction models.

Main Methods:

  • Developed a hierarchical selective attention network.
  • Implemented coarse sentence-level attention for initial sentence selection.
  • Utilized word-level attention and fine sentence-level attention for representation construction and aggregation.

Main Results:

  • The proposed model significantly outperforms existing methods on a standard dataset.
  • Demonstrated improved performance in relation extraction tasks.
  • Achieved higher accuracy with reduced computational load.

Conclusions:

  • The hierarchical selective attention network is effective for distant supervised relation extraction.
  • The model efficiently selects relevant sentences, leading to better performance.
  • This approach offers a more computationally efficient and accurate solution for NLP tasks.