<p>End-to-End Aspect-Based Sentiment Analysis (EABSA) is a pivotal task in fine-grained sentiment mining, which requires the joint extraction of aspect terms and prediction of their associated sentiment polarities from unstructured text. Despite the remarkable progress of generative models in ABSA, existing approaches still suffer from two critical limitations: inadequate capture of task-specific semantic associations and a prevalent over-prediction. These flaws not only hinder the accurate detection of implicit aspect terms but also compromise the reliability of sentiment polarity inference. To address these issues, we propose a novel Hybrid framework with Generation-guided Sequence Tagging (HyGST), which innovatively enhances task-relevant semantics by exploiting the intrinsic correlation between words and labels. HyGST encompasses two primary modules, specifically a generative T5 model and a sequence tagging module. A pre-trained T5 model can provide a context encoder, predicting potential aspect-sentiment pairs to guide subsequent fine-grained modeling. Meanwhile, the sequence tagging module constructs a token-label semantic graph based on the T5 encoder’s outputs, explicitly modeling the relational dependencies between tokens and labels. Finally, a graph convolutional network aggregates contextual information from the constructed semantic graph, while leveraging global label-aware representations to deliver enhanced task-specific semantic supervision for EABSA. Preliminary experiments demonstrate that the proposed framework outperforms current EABSA methods, verifying its effectiveness in addressing the core challenges of EABSA.</p>