## 2 Related Work  
A lot of research work has proved that the method of using neural network model to model the feature representation of word sequence in ABSC can well capture the context information. For example, Convolutional Neural Networks (CNNs) [15; 16], Recurrent Neural Networks (RNNs) [17], or a combination of both Convolutional Recurrent Neural Networks (CRNNs) [18]. Recently, in view of the good performance of the attention mechanism in modeling contextual information, more and more works have begun to consider the use of attention-based neural network models for ABSC. The main idea is to capture and establish the connection between aspects and opinion words through the attention mechanism. Strictly speaking, attention-based neural network models can also be used as a method of using sentence structure information, because the distance between aspects and opinion word is generally not too far. [PERSON] et al. [5] proposed using attention-based to identify important sentiment information related to aspects. [PERSON] et al. [19]introduced a multi-layer attention mechanism to capture the long-distance opinion words of the aspect. For similar purposes, [PERSON] et al. [20] proposed a deep memory network with multi-hop attention and explicit memory. [PERSON] et al. [21] proposed a multi-granularity attention network. In addition, in view of the advantages of the multi-head attention mechanism in modeling contextual semantic relations, [PERSON] et al. [22] proposed an attention encoder network to draw the hidden states and semantic interactions between target and context words. [PERSON] et al. [23] proposed a novel Interactive Dual Attention Network (IDAN) model that aims to interactively learn the representation between contextual semantics and sentimental tendency information. [PERSON] et al. [24] utilizes the transformer module to learn the word-level representations of aspects and context, respectively, and further utilizes the tree transformer module to obtain the phrase-level representations of contexts. In addition, they adopt dual-pooling method and multi-grained attention network to extract high quality aspect-context interactive representations. [PERSON] et al. [25] introduced multi-head interactive attention based on the work of [PERSON] et al. [22] to enhance the interaction between aspect items and context. Other works consider using sentence structure information for ABSC. Aspects are usually the core of this task. Therefore, using sentence structure information to establish connections between aspects and opinion words can improve the performance of sentiment classification models. Since the graph convolutional network [26] was first introduced, due to its excellent performance in processing graph structure information, it has been quickly applied to various tasks in the field of natural language processing (NLP) and achieved good results. [PERSON] and [PERSON] [27] proposed a GCN-based semantic role annotation model. [PERSON] and [PERSON] [28] propose a novel target-dependent graph attention network, which explicitly utilizes the dependency relationship among words. [PERSON] et al. [29] defined a unified aspect-oriented dependency tree structure rooted at a target aspect, and proposed a relational graph attention network (GAT) to encode the new tree structure for sentiment prediction. [PERSON] et al. [30] proposed a dependency graph enhanced dual-transformer network by jointly considering the flat representations learnt from transformer and graph-based representations learnt from the corresponding dependency graph. In addition, [PERSON] et al. [31] constructed three target-dependent variants of the BERT model for target-dependent sentiment classification. [PERSON] and [PERSON] [32] proposed a transfer capsule network model to transfer sentence-level semantic knowledge from document-level sentiment classification to ABSC. [PERSON] et al. [33] proposed a gradual self-supervised attention learning method to strengthen the performance of the attention mechanism. [PERSON] et al.