前后关系化图注意用于增加关系提取(cs.CL)---用户7199428

这篇论文展现了一个前后关系化的图注意网络组合了边的特征和多重子图用于提高关系提取能力。这个新奇的方法旨在使用多重子图在基于图的网络中学习丰富的节点表示。这个终端的多重子图是有一个单一的依赖树获取的。提出了两种类型的边特征，并将它们与GAT和GCN模型有效组合用于关系提取。提出的模型在Semeval2010数据库上的性能堪称世界前沿水平，并且F1-score高达83。

原文题目：Contextualised Graph Attention for Improved Relation Extraction

原文：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.

原文作者：Angrosh Mandya, Danushka Bollegala, Frans Coenen

原文地址：https://arxiv.org/abs/2004.10624