S ^2 GSL : Incorporating Segment to Syntactic Enhanced Graph Structure Learning for Aspect-based Sentiment Analysis (2024)

Abstract: 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.

## introduction aspect-based sentiment analysis (absa) is a finegrained sentiment analysis task that aims to recognize the sentiment polarities of multiple aspects within a given sentence. for example, in the sentence "the falafel was rather overcooked and dried but the chicken was fine," the sentiment polarity of the aspect words "falafel" and "chicken" is recognized as negative and positive, respectively. the absa task presents a notable challenge in accurately recognizing the sentiment polarity of specific aspect words, particularly when they are influenced by other aspect words with contrasting polarities within the given context. the atmosphere is unheralded, the service impecible, but the food terrible. figure 1 : the attention weight of the aspect word "atmosphere" in relation to other words in the sentence."×" refers to noise information for "atmosphere". the atmosphere is unheralded, the service impecible, but the food terrible. leading graph-based approaches tackle absa tasks by learning either prior static structures and learnable dynamic semantic structures via graph neural networks and attention mechanisms. for instance, @xcite proposed combining external syntactic dependency tree and implicit graph to generate aspect-specific representations using gcn; @xcite proposed to construct a semgcn module and a syngcn module using an attention mechanism and a syntactic dependency tree. although promising results were reported, we observe that existing graph structure learning approaches are still prone to incorrect predictions with hard samples containing multiple aspect words. existing solutions introduce a heavy structure learning process leading to the following two main limitations: (i) approaches reliant on attention mechanism are vulnerable to irrelevant context, potentially resulting in misalignment or weak linking. as shown in figure 1 , for the aspect word "atmosphere", except for the clause "the atmosphere is unheralded", the other parts are redundant for judging sentiment polarity, i.e., semantic noise. negatively influenced by irrelevant context, this results in only weak linking to "atmosphere" and the corresponding opinion word "unheralded". (ii) the global structure of the dependency tree for parsing complex long sentences cannot avoid containing irrelevant dependency information for polarity judgment. as shown in figure 2 , the "conj" relation connecting "unheralded" and "terrible" is the syntactic noise for the aspect "atmosphere". thus, the key to tackling these limitations is efficiently dividing a complex sentence into multiple local clauses in the graph structure learning process. in this paper, we propose s 2 gsl, incorporating segment to syntactic enhanced graph structure learning for absa. to minimize the negative impact of irrelevant structures, s 2 gsl introduces constituent trees to decompose the complex structure of the input sentences. to illustrate the role of constituent trees, figure 3 presents a constituent tree and its third-layer segment matrix. this segment matrix can divide a sentence into three semantically complete paragraphs, which help to facilitate the alignment between each aspect and its corresponding opinion and filter out the contextual information unrelated to the respective paragraph. specifically, we devise a segment-aware semantic graph(sesg) branch by using a supervised dynamic local attention on the constituent tree, to learn the local semantic structure of each aspect. sharing the same idea with leading graph-based approaches, s 2 gsl has also been designed with a syntax-based latent graph(sylg) branch that utilizes syntactic dependency labels to enhance the latent tree construction. the difference from past work is that we introduce an attention-based learning mechanism in sylg that effectively eliminates irrelevant dependency structures. finally, the self-adaptive aggregation network will fuse the sesg branch and sylg branch by cross-attention aggregation mechanism, which considers the complementarity across diverse structures. our proposed s 2 gsl framework makes the following contributions: the atmosphere is unheralded , the service impecible , but the food terrible. the atmosphere is unheralded , the service impecible , but the food terrible. the atmosphere is unheralded , the service impecible , but the food terrible. a learnable method to incorporate syntactic dependencies into latent tree construction. ## proposed s gsl the overall architecture of s 2 gsl is shown in figure 4 which is mainly composed of four modules: context encoding module, segment-aware semantic graph learning(sesg), syntax-based latent graph learning(sylg), and self-adaptive aggregation module. next, components of s 2 gsl will be introduced separately in the rest of the sections. ## related work with the rapid development of absa, current research can be broadly divided into three main categories attention-based methods, syntactic-based methods, and multi-graph combined methods. ## conclusion in this paper, we propose an s 2 gsl model to tackle global structures that will introduce irrelevant contexts and syntactic dependencies during the process of graph structure learning. we propose a sesg branch that decomposes the absa complex graph structure learning problem into multiple local substructure learning processes by utilizing constituent trees. moreover, we propose a sylg branch, a more learnable method to introduce syntactic dependencies into latent tree construction. finally, we devise a self-adaptive aggregation network to realize the interaction between two graph branches, achieving complementarity across diverse structures. experiments on four benchmarks demonstrate s 2 gsl outperforms the baselines.