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Related Experiment Videos

Supervised attention method with co-reference resolution for aspect extraction.

Ganpat Singh Chauhan1, Shalini Puri2, Ravi Nahta3

  • 1Department of Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, India.

Scientific Reports
|May 16, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new model for aspect-based sentiment analysis (ABSA) that improves aspect term detection in multi-sentence reviews. By incorporating co-referencing resolution, the model better captures contextual dependencies for more accurate sentiment analysis.

Keywords:
Aspect term extractionAspect-based sentiment analysisCo-reference resolutionHierarchical attentionSustainable developmentSustainable learning

Related Experiment Videos

Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Aspect-based sentiment analysis (ABSA) faces challenges in multi-sentence reviews due to isolated aspect extraction.
  • Existing methods struggle with long-range dependencies and multi-word aspect terms, leading to incomplete aspect identification.

Purpose of the Study:

  • To propose a supervised hierarchical attention-based model for enhanced aspect term detection in multi-sentence reviews.
  • To address limitations in capturing contextual interdependence and multi-word aspects.

Main Methods:

  • A co-referencing resolution (CRR)-based sentence-alignment mechanism is integrated before aspect-based sentiment analysis (ABSA).
  • A hierarchical attention network (HAN) processes semantically aligned inputs, utilizing word-level and sentence-level attention.
  • Contextual representations are learned using enriched word vectors and sequence-tagged labeled data.

Main Results:

  • The proposed model consistently outperforms strong baselines and state-of-the-art methods on Laptop and Restaurant datasets.
  • Experimental evaluation shows significant F-Score improvements over AspectGCN and IDGNN+BERT.
  • The inclusion of the CRR module provides an additional 3-4% gain over the HAN baseline.

Conclusions:

  • The developed model effectively enhances aspect term extraction (ATE) from multi-sentence reviews.
  • Context-aware and alignment-driven modeling significantly improves performance in ABSA tasks.
  • The CRR-enhanced HAN model offers a robust solution for complex review analysis.