用于事件表示学习的半监督图注意网络

João Pedro Rodrigues Mattos, R. Marcacini
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引用次数: 1

摘要

来自新闻和社交网络的事件分析对于广泛的社会研究和现实世界的应用非常有用。最近,事件图被用于对事件数据集及其复杂关系进行建模,其中事件是连接到其他顶点的顶点,这些顶点表示位置、人名、日期和各种其他事件元数据。图表示学习方法有望从事件图中提取潜在特征,以便使用不同的分类算法。然而,现有的方法无法满足事件图的基本要求,例如(i)处理半监督图嵌入以利用一些标记事件,(ii)自动确定事件顶点与其元数据顶点之间关系的重要性,以及(iii)处理图的异构性。本文提出了一种将图注意网络和图正则化相结合的神经事件嵌入方法GNEE。首先,提出了一种事件图正则化方法,保证图的所有顶点都能接收到事件特征,从而减轻了图的异构性。其次,基于自注意机制的半监督图嵌入考虑了已有的标记事件,并在表示学习过程中学习事件图中关系的重要性。对五种真实事件图和六种图嵌入方法的实验结果进行统计分析表明,我们的GNEE优于最先进的半监督图嵌入方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Semi-Supervised Graph Attention Networks for Event Representation Learning
Event analysis from news and social networks is very useful for a wide range of social studies and real-world applications. Recently, event graphs have been explored to model event datasets and their complex relationships, where events are vertices connected to other vertices representing locations, people’s names, dates, and various other event metadata. Graph representation learning methods are promising for extracting latent features from event graphs to enable the use of different classification algorithms. However, existing methods fail to meet essential requirements for event graphs, such as (i) dealing with semi-supervised graph embedding to take advantage of some labeled events, (ii) automatically determining the importance of the relationships between event vertices and their metadata vertices, as well as (iii) dealing with the graph heterogeneity. This paper presents GNEE (GAT Neural Event Embeddings), a method that combines Graph Attention Networks and Graph Regularization. First, an event graph regularization is proposed to ensure that all graph vertices receive event features, thereby mitigating the graph heterogeneity drawback. Second, semi-supervised graph embedding with self-attention mechanism considers existing labeled events, as well as learns the importance of relationships in the event graph during the representation learning process. A statistical analysis of experimental results with five real-world event graphs and six graph embedding methods shows that our GNEE outperforms state-of-the-art semi-supervised graph embedding methods.
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