Aspect-based sentiment Analysis (ABSA) identifies and evaluates sentiments toward specific aspects of entities within text, providing detailed insights beyond overall sentiment. However, Attention mechanisms and neural network models struggle with syntactic constraints, and the quadratic complexity of attention mechanisms hinders their adoption for capturing long-range dependencies between aspect and opinion words in ABSA. This complexity can lead to the misinterpretation of irrelevant con-textual words, restricting their effectiveness to short-range dependencies. Some studies have investigated merging semantic and syntactic approaches but face challenges in effectively integrating these methods. To address the above problems, we present MambaForGCN, a novel approach to enhance short and long-range dependencies between aspect and opinion words in ABSA. This innovative approach incorporates syntax-based Graph Convolutional Network (SynGCN) and MambaFormer (Mamba-Transformer) modules to encode input with dependency relations and semantic information. The Multihead Attention (MHA) and Mamba blocks in the MambaFormer module serve as channels to enhance the model with short and long-range dependencies between aspect and opinion words. We also introduce the Kolmogorov-Arnold Networks (KANs) gated fusion, an adaptively integrated feature representation system combining SynGCN and MambaFormer representations. Experimental results on three benchmark datasets demonstrate MambaForGCN's effectiveness, outperforming state-of-the-art (SOTA) baseline models.

翻译：方面级情感分析旨在识别并评估文本中针对实体特定方面的情感，提供超越整体情感的细致洞察。然而，注意力机制与神经网络模型受限于句法约束，且注意力机制的二次计算复杂度阻碍了其在捕捉方面词与观点词之间长程依赖关系中的应用。这种复杂性可能导致对无关上下文词的误判，使其有效作用范围局限于短程依赖。已有研究尝试融合语义与句法方法，但在有效整合这些方法方面面临挑战。为解决上述问题，本文提出MambaForGCN——一种增强方面级情感分析中方面词与观点词间短程与长程依赖关系的新方法。该创新方法融合基于句法的图卷积网络与MambaFormer模块，通过依存关系与语义信息对输入进行编码。MambaFormer模块中的多头注意力机制与Mamba块作为增强模型对方面词与观点词间短程与长程依赖关系的通道。我们还引入了Kolmogorov-Arnold网络门控融合机制，这是一个自适应整合SynGCN与MambaFormer表征的特征表示系统。在三个基准数据集上的实验结果表明，MambaForGCN具有显著有效性，其性能优于当前最先进的基线模型。