Aspect-based sentiment classification (ASC) aims to judge the sentiment polarity conveyed by the given aspect term in a sentence. The sentiment polarity is not only determined by the local context but also related to the words far away from the given aspect term. Most recent efforts related to the attention-based models can not sufficiently distinguish which words they should pay more attention to in some cases. Meanwhile, graph-based models are coming into ASC to encode syntactic dependency tree information. But these models do not fully leverage syntactic dependency trees as they neglect to incorporate dependency relation tag information into representation learning effectively. In this paper, we address these problems by effectively modeling the local and global features. Firstly, we design a local encoder containing: a Gaussian mask layer and a covariance self-attention layer. The Gaussian mask layer tends to adjust the receptive field around aspect terms adaptively to deemphasize the effects of unrelated words and pay more attention to local information. The covariance self-attention layer can distinguish the attention weights of different words more obviously. Furthermore, we propose a dual-level graph attention network as a global encoder by fully employing dependency tag information to capture long-distance information effectively. Our model achieves state-of-the-art performance on both SemEval 2014 and Twitter datasets.

翻译：方面情感分类（ASC）旨在判断句子中给定方面词所传达的情感极性。情感极性不仅由局部上下文决定，还受远离该方面词的词汇影响。现有基于注意力机制的最新研究在某些情况下难以充分区分应重点关注的词汇。同时，基于图的模型开始应用于ASC以编码句法依存树信息，但这些模型未能充分利用句法依存树——它们未能将依存关系标签信息有效融入表示学习中。本文通过有效建模局部与全局特征解决上述问题。首先，我们设计包含高斯掩膜层和协方差自注意力层的局部编码器：高斯掩膜层可自适应调整方面词周围的感受野，弱化无关词汇的影响并强化局部信息关注；协方差自注意力层能更显著地区分不同词汇的注意力权重。此外，我们提出一种双层图注意力网络作为全局编码器，通过充分利用依存标签信息有效捕获长距离信息。本模型在SemEval 2014和Twitter数据集上均取得了最优性能。