基于图卷积记忆网络的方面级情感分类

在方面级情感分类中,常用的方法是用卷积神经网络或循环神经网络提取特征,利用注意力权重获取序列中不同词汇的重要程度。但此类方法未能很好地利用文本的句法信息,导致模型不能准确地在评价词与方面词之间建立联系。该文提出一种图卷积神经记忆网络模型(MemGCN)来解决此依赖问题。首先通过记忆网络存储文本表示与辅助信息,然后利用基于依存句法树的图卷积神经网络获取文本的句法信息。最后,使用注意力机制融合句法信息与其他辅助信息。在SemEval 2014任务和Twitter数据集上的实验结果表明,MemGCN显著提升了模型性能。

Aspect-based Sentiment Classification via Memory Graph Convolutional Network

In aspect-based sentiment classification, the attention mechanism is often combined in recurrent neural network or convolutional neural network to obtain the importance of different words. However, such kind of methods fail to capture long-range syntactic relations that are obscure from the surface form, which would be beneficial to identify sentiment features directly related to the aspect target. In this paper, we propose a novel model named MemGCN to explicitly utilize the dependency relationship among words. Firstly, we employ the memory network to obtain the context-aware memory representation. After that, we apply graph convolutional network over the dependency tree to propagate sentiment features directly from the syntactic context of an aspect target. Finally, the attention mechanism is used to fuse memory and syntactic information. Experiment results on SemEval 2014 and Twitter datasets demonstrate our model outperforms baseline methods.