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.

提出了一个新的方法 S$^2$GSL，在 Aspect based Sentiment Analysis (ABSA) 中结合了段落感知的语义图学习和基于语法的潜在图学习，以提高图结构学习的准确性和效果。