Abstract: Aspect-based sentiment analysis (ABSA) aims to judge the sentiment polarity of specific aspects in texts. Existing methods usually adopt graph neural networks and attention mechanisms to encode the syntactic dependency information and semantic information of sentences. However, only the dependency relations between words can be captured by the syntactic dependency tree, and phrase-level syntactic structure cannot be expressed, thus limiting the model's utilization of phrase-level syntactic information. Moreover, when attention mechanisms are used to capture the semantic features of sentences, they are usually interfered by irrelevant contexts, thus generating semantic noise. Therefore, this paper proposes an aspect-based sentiment analysis model based on syntactic enhancement and semantic denoising. In the syntactic branch, a syntactic constituent tree is introduced to construct a syntactic constituent graph, so as to supplement phrase-level syntactic information. Two types of syntactic information are encoded by the syntactic constituent graph and the syntactic dependency graph respectively, and are dynamically weighted and aggregated through a syntactic fusion mechanism to obtain syntactically enhanced representations. In the semantic branch, a differential attention mechanism is introduced to reduce the attention weights of irrelevant contexts, thereby reducing semantic noise and obtaining denoised semantic representations. In addition, representations fused with external knowledge are obtained by concatenating external knowledge embeddings at the end of word embeddings, so as to help the model better understand sentence semantics. Finally, a multi-feature fusion module is used to fully integrate the three features. Experimental results show that compared with baseline models such as S2GSL, the proposed model improves the accuracy by at least 0.36, 0.83 and 3.13 percentage points on the Laptop, Restaurant and Twitter datasets, respectively, and boosts the F1-score by at least 0.56 and 2.96 percentage points on the Laptop and Twitter datasets, respectively, which verifies the effectiveness of the model’s syntactic enhancement and semantic denoising methods.

摘要： 方面级情感分析旨在对文本中特定方面的情感极性做出判断。现有的方法往往采用图神经网络和注意力机制来编码句子的句法依赖信息和语义信息，然而，句法依赖树仅能捕获词语之间的依存关系，无法表达短语级句法结构，限制了模型对短语级句法信息的利用；并且在使用常规softmax注意力机制捕获句子的语义特征时，通常会受到无关上下文的干扰，从而产生过多语义噪声。因此，本文提出一个基于句法增强和语义降噪的方面级情感分析模型。在句法分支中，引入句法成分树构建句法成分图，以补充短语级句法信息，利用句法成分图和句法依赖图分别编码两种句法信息，并通过句法融合机制动态整合两种信息，得到句法增强表征；在语义分支中，引入差分注意力机制降低无关上下文的注意力权重，从而降低语义噪声，得到降噪后的语义表征；另外，通过在词嵌入末端拼接外部知识嵌入得到融合外部知识的表征，以帮助模型更好地理解句子语义；最后利用多特征融合模块将三种特征进行充分融合。实验结果表明，相较于S2GSL等基线模型，本文模型在Laptop、Restaurant和Twitter数据集上的准确率分别提高至少0.36、0.83和3.13个百分点，在Laptop和Twitter数据集上的F1分数分别提高至少0.56和2.96个百分点，验证了本文模型句法增强和语义降噪方法的有效性。