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A multi-scale local attention mechanism for aspect extraction.

Qian Yang1,2, Jinsen Zhu1,2, Hongyu Du1,2

  • 1School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.

Scientific Reports
|July 2, 2025
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Summary

This study introduces a novel multi-scale local attention (MLA) mechanism for aspect extraction, improving knowledge graph construction. The MLA method enhances accuracy in identifying key features from unstructured text.

Keywords:
Attention mechanismChinese-oriented aspect extractionGated recurrent unitPre-trained model

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Aspect extraction is crucial for knowledge graph construction from unstructured text.
  • Existing attention-based methods (global, local) have limitations like noise and optimal window size determination.
  • These limitations hinder precise aspect information retrieval.

Purpose of the Study:

  • To propose a novel aspect extraction approach using a multi-scale local attention (MLA) mechanism.
  • To overcome the limitations of existing global and local attention methods.
  • To improve the accuracy and performance of aspect extraction.

Main Methods:

  • Utilized a pre-trained model for text vector representation.
  • Employed gated recurrent units for feature extraction.
  • Applied multi-scale local attention (MLA) for representation learning across various window sizes.
  • Used max pooling for feature selection and a fully connected neural network with a conditional random field for precise aspect labeling.

Main Results:

  • The proposed MLA method demonstrated superior performance in aspect extraction.
  • Experimental validation was conducted on the Zhejiang Cup e-commerce review mining dataset.
  • The approach outperformed existing models in aspect extraction tasks.

Conclusions:

  • The multi-scale local attention (MLA) mechanism is an effective approach for aspect extraction.
  • This method offers significant improvements over traditional attention mechanisms.
  • The findings contribute to more accurate knowledge graph construction and information retrieval.