Previous graph-based approaches in Aspect based Sentiment Analysis(ABSA) have demonstrated impressive performance by utilizing graph neural networks and attention mechanisms to learn structures of static dependency trees and dynamic latent trees. However, incorporating both semantic and syntactic information simultaneously within complex global structures can introduce irrelevant contexts and syntactic dependencies during the process of graph structure learning, potentially resulting in inaccurate predictions. In order to address the issues above, we propose S$^2$GSL, incorporating Segment to Syntactic enhanced Graph Structure Learning for ABSA. Specifically,S$^2$GSL is featured with a segment-aware semantic graph learning and a syntax-based latent graph learning enabling the removal of irrelevant contexts and dependencies, respectively. We further propose a self-adaptive aggregation network that facilitates the fusion of two graph learning branches, thereby achieving complementarity across diverse structures. Experimental results on four benchmarks demonstrate the effectiveness of our framework.

翻译：先前基于图的面向方面情感分析方法通过利用图神经网络和注意力机制学习静态依存树与动态潜在树的结构，已展现出显著性能。然而，在复杂全局结构中同时融合语义与句法信息，会在图结构学习过程中引入无关上下文与句法依赖，可能导致预测偏差。为解决上述问题，我们提出S$^2$GSL——融入分段增强句法的图结构学习用于面向方面情感分析。具体而言，S$^2$GSL通过分段感知语义图学习与基于句法的潜在图学习，分别实现无关上下文与依赖关系的去除。我们进一步提出自适应聚合网络，促进两个图学习分支的融合，从而在不同结构间实现互补。在四个基准数据集上的实验结果验证了本框架的有效性。