Aspect-based Sentiment Analysis (ABSA) evaluates sentiment expressions within a text to comprehend sentiment information. Previous studies integrated external knowledge, such as knowledge graphs, to enhance the semantic features in ABSA models. Recent research has examined the use of Graph Neural Networks (GNNs) on dependency and constituent trees for syntactic analysis. With the ongoing development of ABSA, more innovative linguistic and structural features are being incorporated (e.g. latent graph), but this also introduces complexity and confusion. As of now, a scalable framework for integrating diverse linguistic and structural features into ABSA does not exist. This paper presents the Extensible Multi-Granularity Fusion (EMGF) network, which integrates information from dependency and constituent syntactic, attention semantic , and external knowledge graphs. EMGF, equipped with multi-anchor triplet learning and orthogonal projection, efficiently harnesses the combined potential of each granularity feature and their synergistic interactions, resulting in a cumulative effect without additional computational expenses. Experimental findings on SemEval 2014 and Twitter datasets confirm EMGF's superiority over existing ABSA methods.

翻译：基于方面的情感分析（ABSA）旨在评估文本中的情感表达，以理解情感信息。以往的研究通过整合外部知识（如知识图谱）来增强ABSA模型中的语义特征。近期研究探索了在依存句法树和成分句法树上使用图神经网络（GNNs）进行句法分析。随着ABSA的持续发展，更多创新的语言和结构特征（如潜在图）被引入，但这同时也带来了复杂性和混乱。截至目前，尚不存在一个可扩展的框架来整合ABSA中的多样化语言和结构特征。本文提出了可扩展多粒度融合（EMGF）网络，该网络融合了依存句法与成分句法信息、注意力语义信息以及外部知识图谱信息。配备多锚点三元组学习和正交投影的EMGF，能够高效利用各粒度特征及其协同作用的组合潜力，在不增加额外计算开销的情况下产生累积效应。在SemEval 2014和Twitter数据集上的实验结果证实了EMGF相较于现有ABSA方法的优越性。