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Block-level dependency syntax based model for end-to-end aspect-based sentiment analysis.

Yan Xiang1, Jiqun Zhang1, Junjun Guo1

  • 1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China; Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, 650500, China.

Neural Networks : the Official Journal of the International Neural Network Society
|July 29, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Block-Level Dependency Syntax Parsing (BDEP) model for End-to-End Aspect-Based Sentiment Analysis (E2E-ABSA). The BDEP model significantly enhances the extraction of aspect terms and sentiment polarity, outperforming existing methods.

Keywords:
Adaptive semantic-syntactic fusionAspect termAspect-based sentiment analysisBlock-dependency syntax-guided interactive attentionBlock-level dependency syntax

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • End-to-End Aspect-Based Sentiment Analysis (E2E-ABSA) aims to extract aspect terms and their sentiment polarities simultaneously.
  • Existing syntax dependency parsing methods struggle to capture crucial block-level relationships between aspect and opinion terms, limiting their effectiveness in E2E-ABSA.
  • There is a need for improved syntactic information integration to enhance E2E-ABSA performance.

Purpose of the Study:

  • To propose a novel Block-Level Dependency Syntax Parsing (BDEP) based model to improve E2E-ABSA.
  • To effectively capture block-level relations between aspect and opinion terms using enhanced syntax parsing.
  • To simultaneously extract aspect terms and determine their sentiment polarities with higher accuracy.

Main Methods:

  • Developed a Block-Level Dependency Syntax Parsing (BDEP) approach by integrating routine dependency parsing and part-of-speech tagging.
  • Introduced a BDEP-guided interactive attention module (BDEP-IAM) to generate aspect-aware word representations.
  • Utilized an adaptive fusion module to combine semantic and syntactic information for joint aspect extraction and sentiment analysis.

Main Results:

  • The proposed model achieved state-of-the-art (SOTA) performance on five benchmark datasets: Laptop14 (62.67% F1), Rest_ALL (76.53% F1), Restaurant14 (75.42% F1), Restaurant15 (62.21% F1), and TWITTER (58.03% F1).
  • The model consistently outperformed existing SOTA methods across all evaluated datasets.
  • Ablation experiments validated the significant contributions of the BDEP and BDEP-IAM components to the overall performance improvement.

Conclusions:

  • The BDEP model effectively addresses the limitations of standard dependency parsing in E2E-ABSA by capturing block-level syntactic relations.
  • The integration of block-level syntax and attention mechanisms leads to superior performance in joint aspect term extraction and sentiment polarity identification.
  • This research offers a promising direction for advancing E2E-ABSA through sophisticated syntactic information processing.