论文标题
上下文化的图形注意力以改善关系提取
Contextualised Graph Attention for Improved Relation Extraction
论文作者
论文摘要
本文提出了一个上下文化的图形注意网络，该网络结合了边缘特征和多个子图，以改善关系提取。提出了一种新的方法，可以使用多个子图来学习基于图的网络中的丰富节点表示。为此，多个子图是从单个依赖树获得的。提出了两种类型的边缘特征，它们与GAT和GCN模型有效合并以申请关系提取。所提出的模型在Semeval 2010 Task 8数据集上实现了最先进的性能，达到了86.3的F1得分。
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.
