Aspect Sentiment Triplet Extraction (ASTE) is a fine-grained task in Aspect-Based Sentiment Analysis (ABSA), aiming to extract triplets of aspect terms, their corresponding opinion terms, and sentiment orientation. Recent studies have employed semantic and syntactic features to enhance extraction performance. However, as the extraction of both syntactic and semantic information is not task-specific, such information does not target aspect-opinion pairs within sentences, thereby potentially introducing noise unrelated to the task. To address this issue, we propose a Syntactic-Guided Semantic Attention Matrix Enhancement (SG-SAME) framework. First, we use a multi-view masking mechanism and a biaffine-based gating module to derive a task-aligned shared matrix from the existing semantic and syntactic matrices. Second, the shared matrix is injected into the attention score matrix of LLM layers to enhance the model’s representation learning. Finally, we propose a chain-of-thought (CoT) framework with negative candidate injection to steer the model toward autonomous reasoning and error self-rectification. Experimental results on multiple benchmark datasets demonstrate that our method outperforms existing state-of-the-art approaches in ASTE tasks. Code will be available.

Multimodal content combining textual and visual information poses significant challenges for rumor detection on social media. Compared to traditional spatial domain features, frequency domain features have attracted increasing attention due to their stronger discriminative capabilities. However, existing methods still fall short in capturing cross-modal semantic inconsistencies and often overlook inherent noise in multimodal features, which limits overall detection performance. To address these issues, we propose a novel multimodal rumor detection method based on multi-scale spectral selection and entropy-guided uncertainty fusion. Specifically, we first apply the Discrete Cosine Transform (DCT) to image and text features to convert them into the frequency domain. Then, multi-scale convolutional filters are employed to extract fine-grained information across different frequency scales. Next, modality separation is performed to capture both shared and modality-specific features, enabling more effective cross-modal representation learning. Finally, entropy is used to estimate the uncertainty of each prediction branch, calculate confidence scores, and perform adaptive weighted fusion accordingly. Experimental results on multiple benchmark datasets demonstrate that our method outperforms existing state-of-the-art approaches in multimodal rumor detection, demonstrating stronger detection capability and robustness.