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A hybrid attention and dilated convolution framework for entity and relation extraction and mining.

Yuxiang Shan1, Hailiang Lu1, Weidong Lou2

  • 1China Tobacco Zhejiang Industrial Company Limited, Hangzhou, 311500, China.

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Summary

This study introduces a novel hybrid attention and dilated convolution network (HADNet) for efficient entity and relation extraction. HADNet improves knowledge graph construction by addressing computation efficiency and relation prediction redundancy.

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Data Mining

Background:

  • Knowledge graph construction and expansion rely on mining entities and relations from unstructured text.
  • Existing methods face challenges in computational efficiency and relation prediction redundancy.

Purpose of the Study:

  • To propose a novel hybrid attention and dilated convolution network (HADNet) for efficient end-to-end entity and relation extraction.
  • To enhance computation efficiency and address redundancy in relation prediction for knowledge graph mining.

Main Methods:

  • Developed a novel encoder architecture integrating attention mechanism, dilated convolutions, and a gated unit.
  • Implemented a three-phase decoder for relation prediction, entity recognition, and relation determination.
  • Evaluated the HADNet model on two public real-world datasets.

Main Results:

  • The proposed HADNet demonstrates improved computation efficiency through its integrated encoder architecture.
  • The model effectively achieves a global receptive field while preserving local context.
  • Experimental results confirm the effectiveness of HADNet in entity and relation extraction tasks.

Conclusions:

  • HADNet offers an effective end-to-end solution for entity and relation mining.
  • The model's architecture enhances computational efficiency and accuracy in knowledge graph construction.
  • HADNet represents a significant advancement in overcoming limitations of previous approaches.