多目标依存建模在特定目标情感分类中的应用

特定目标情感分类旨在准确判别句子中目标的情感极性,现有的方法大多只对单一目标进行分析,而忽略了同一句中多个目标之间存在的依存性。为了有效建模目标之间的依存性,该文提出一种基于多目标依存建模的图卷积网络模型。首先,通过注意力机制对目标进行上下文语义编码;然后,根据句子的依存句法树构建多目标依存图,再根据多目标依存图使用图卷积网络对多个目标之间的依存性进行建模;最后,利用生成的目标表示进行情感分类。该模型在SemEval 2014 Task4 Restaurant和Laptop两个数据集上进行实验,结果表明,该文模型相比基于标准图卷积网络的模型性能有显著提高,在特定目标情感分类任务中更具竞争力。

Application of Modeling Multi-aspects Dependencies in Aspect-level Sentiment Classification

Aspect-level sentiment classification aims to accurately identify the emotional polarity of aspects in a sentence. In order to effectively model the dependencies between multi-aspects in one sentence, this paper proposes a graph convolution network (GCN) approach. First, the aspects are encoded with the context by attention mechanism. Then, the multi-aspects dependency graph is constructed from the dependency syntax tree, and GCN is applied on the graph to model the dependencies between multi-aspects in one sentence. Finally, sentiment classification is preformed using the aspect representation generated by the GCN. Experiments on the Restaurant and Laptop datasets of SemEval 2014 Task4 show that the proposed model achieves a significant improvement over the standard GCN models.