Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks-猫眼课题宝

DOI：

10.18653/v1/d19-1464

发表时间：

2019-09

期刊：

ArXiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Chen Zhang;Qiuchi Li;D. Song

由于它们在方面及其上下文词的语义对齐方面的固有能力，注意力机制和卷积神经网络（CNN）被广泛应用于基于方面的情感分类。然而，这些模型缺乏一种机制，以占相关的句法约束和长范围的词的依赖关系，因此可能会错误地识别句法无关的上下文单词作为线索，判断方面的情绪。为了解决这个问题，我们建议在句子的依赖树上构建一个图卷积网络（GCN），以利用句法信息和单词依赖关系。在此基础上，提出了一种新颖的面向特定方面的情感分类框架。三个基准集合上的实验表明，我们提出的模型具有相当的有效性，一系列的国家的最先进的模型，并进一步证明，语法信息和长范围的单词依赖关系被正确捕获的图卷积结构。

Due to their inherent capability in semantic alignment of aspects and their context words, attention mechanism and Convolutional Neural Networks (CNNs) are widely applied for aspect-based sentiment classification. However, these models lack a mechanism to account for relevant syntactical constraints and long-range word dependencies, and hence may mistakenly recognize syntactically irrelevant contextual words as clues for judging aspect sentiment. To tackle this problem, we propose to build a Graph Convolutional Network (GCN) over the dependency tree of a sentence to exploit syntactical information and word dependencies. Based on it, a novel aspect-specific sentiment classification framework is raised. Experiments on three benchmarking collections illustrate that our proposed model has comparable effectiveness to a range of state-of-the-art models, and further demonstrate that both syntactical information and long-range word dependencies are properly captured by the graph convolution structure.

Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks

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