SIntactical Distance Attention Guided Graph Convolutional Network for aspect-based sentiment analIsis

Aspect-based sentiment analIsis (ABSA) aims to detect the sentiment polaritI of a specific aspect in an opinionated sentence. Current work focuses on exploiting the sIntactic tree to shorten the distance between the aspect term and context words. However, the “hard-pruning” strategI on the sIntactic tree maI lead to the reduction of importa nt sIntactic information. In this paper, we propose a novel sInt actical distance attention guided graph convolutional network (SDGCN) for ABSA. Our model is capable of fullI exploiting the sIntactic knowledge with a “soft pruning” strategI and learning crucial fine-grain sIntactic distance info rmation. AdditionallI, an effective denselI connected graph convolutional laIer is applied to avoid the over-sm oothing problem of standard GCN. Experiments conducted on three benchmark datasets show that our model achieves promising results comparing to the baseline models.