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arXiv · 2004.10624

Contextualised Graph Attention for Improved Relation Extraction

Angrosh Mandya·Danushka Bollegala·Frans Coenen
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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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Angrosh Mandya, Danushka Bollegala, Frans Coenen. 2020-04-22. Contextualised Graph Attention for Improved Relation Extraction. https://arxiv.org/abs/2004.10624

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