Probabilistic Syntax-Aware Joint Span–Sentiment Learning for Aspect-Based Sentiment Analysis

Abstract

Aspect-Based Sentiment Analysis (ABSA) aims to identify aspect terms in text and predict their sentiment polarities. The two subtasks are tightly coupled: inaccurate aspect boundaries can mislead sentiment prediction, while sentiment supervision should in turn refine span selection. However, existing methods still suffer from two limitations. First, although boundary detection and sentiment classification are interdependent, gradients between them are often weak, resulting in error propagation, especially in pipeline settings. Second, under complex syntactic phenomena such as negation and contrast, boundary shifts and the mismatch between training on gold spans and inferring on predicted spans cause a train–inference discrepancy and unstable optimization. To address these issues, we propose a Probabilistic Syntax-Aware Joint Span–Sentiment Learning (PSJL) approach for ABSA. PSJL relaxes discrete span selection into a differentiable span distribution and dynamically incorporates syntactic constraints for end-to-end optimization. Specifically, (i) Probabilistic Joint Modeling (PJM) computes expectation-based span representations from a span distribution, enabling sentiment loss to backpropagate to boundary prediction. (ii) Syntax-Constrained Contrastive Learning (SCCL) derives span-level masks from dependency parse trees to contrast syntactically plausible and implausible candidates, regularizing boundary decisions. (iii) Adaptive Training Strategy (ATS) adaptively balances supervision from gold and predicted spans based on a syntax-consistency rate, mitigating train–inference discrepancy and improving training stability. Experiments on three benchmark datasets show that PSJL consistently outperforms state-of-the-art models and strong LLM-based baselines on aspect term extraction and sentiment classification.