Compared to sequential learning models, graph-based neural networks exhibit some excellent properties, such as ability capturing global information. In this paper, we investigate graph-based neural networks for text classification problem. A new framework TensorGCN (tensor graph convolutional networks), is presented for this task. A text graph tensor is firstly constructed to describe semantic, syntactic, and sequential contextual information. Then, two kinds of propagation learning perform on the text graph tensor. The first is intra-graph propagation used for aggregating information from neighborhood nodes in a single graph. The second is inter-graph propagation used for harmonizing heterogeneous information between graphs. Extensive experiments are conducted on benchmark datasets, and the results illustrate the effectiveness of our proposed framework. Our proposed TensorGCN presents an effective way to harmonize and integrate heterogeneous information from different kinds of graphs.

中文翻译：

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用于文本分类的张量图卷积网络

与顺序学习模型相比，基于图的神经网络表现出一些优异的特性，例如捕获全局信息的能力。在本文中，我们研究了用于文本分类问题的基于图的神经网络。针对此任务提出了一个新框架 TensorGCN（张量图卷积网络）。首先构造文本图张量来描述语义、句法和顺序上下文信息。然后，对文本图张量进行两种传播学习。第一个是图内传播，用于从单个图中的邻域节点聚合信息。第二个是图间传播，用于协调图之间的异构信息。在基准数据集上进行了广泛的实验，结果说明了我们提出的框架的有效性。我们提出的 TensorGCN 提供了一种有效的方法来协调和整合来自不同类型图的异构信息。