Dependency-enhanced graph convolutional networks for aspect-based sentiment analysis

Aspect-based sentiment analysis aims to extract aspect and opinion terms, and identify the sentiment polarities for such terms. The majority of research has proposed effective methods in individual subtasks, and some multi-task learning models have been designed to deal with combining two subtasks, such as extracting aspect terms and opinions in pairs. Recently, there have been some studies on triple extraction tasks that attempt to simultaneously extract target terms (aspects, opinions) and sentiment polarities from a sentence. However, these studies ignore the directional dependency relations between terms and context, and the intrinsic dependence between these terms has not been well exploited. In this paper, we propose a novel dependency-enhanced graph convolutional network (DE-GCN) for multi-variate extraction tasks. We re-integrate the directional dependency relations in the graph convolution to reconstruct the time-series information representation. In addition, we construct a dependency aggregator to enhance dependency relations between contexts. We conduct experiments on extensive experiments and comparisons on these subtasks. Experimental results on four datasets show the effectiveness of our proposed model.