Contextualised Graph Attention for Improved Relation Extraction

• URL: http://arxiv.org/abs/2004.10624v1
• Date: Wed, 22 Apr 2020 15:04:52 GMT
• Title: Contextualised Graph Attention for Improved Relation Extraction
• Authors: Angrosh Mandya, Danushka Bollegala and Frans Coenen
• Abstract summary: A novel method is proposed to use multiple sub-graphs to learn rich node representations in graph-based networks. 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.
• Score: 18.435408046826048
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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