Span-based semantic syntactic dual enhancement for aspect sentiment triplet extraction

Abstract

Aspect-Based Sentiment Triple Extraction (ASTE), a critical sub-task of Aspect-Based Sentiment Analysis (ABSA), has received extensive attention in recent years. ASTE aims to extract structured sentiment triples from texts, with most existing studies focusing on designing new strategic frameworks. Nonetheless, these methods often overlook the complex characteristics of linguistic expression and the deeper semantic nuances, leading to deficiencies in extracting the semantic representations of triples and effectively utilizing syntactic relationships in texts. To address these challenges, this paper introduces a span-based semantic and syntactic Dual-Enhanced model that deeply integrates rich syntactic information, such as part-of-speech tagging, constituent syntax, and dependency syntax structures. Specifically, we designed a semantic encoder and a syntactic encoder to capture the semantic-syntactic information closely related to the sentence’s underlying intent. Through a Feature Interaction Module, we effectively integrate information across different dimensions and promote a more comprehensive understanding of the relationships between aspects and opinions. We also adopted a span-based tagging scheme that generates more precise aspect sentiment triple extractions by exploring cross-level information and constraints. Experimental results on benchmark datasets derived from the SemEval challenge prove that our model significantly outperforms existing baselines.