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.
@article{arxiv.2004.10624,
title = {Contextualised Graph Attention for Improved Relation Extraction},
author = {Angrosh Mandya and Danushka Bollegala and Frans Coenen},
journal= {arXiv preprint arXiv:2004.10624},
year = {2020}
}