  This paper presents a contextualized graph attention network that combines
edge features and multiple sub-graphs for improving relation extraction. A
novel method is proposed to use multiple sub-graphs to learn rich node
representations in graph-based networks. To this end multiple sub-graphs are
obtained from a single dependency tree. Two types of edge features are
proposed, which are effectively combined with GAT and GCN models to apply for
relation extraction. The proposed model achieves state-of-the-art performance
on Semeval 2010 Task 8 dataset, achieving an F1-score of 86.3.
