Text Classification Based on Label Data Augmentation and Graph Neural Network

Abstract

Although graph neural networks based methods can solve the uneven text length problem of text classification datasets, they are difficult to address the data sparsity problem of short texts. Although some researchers try to reduce the sparsity of the graph by adding labels to its structure, most of them only treat labels as node features other than words and documents, which is not sufficient to construct denser matrices. To address the above problems, three label data augmentation strategies are proposed to build a dense graph, and the attention mechanisms are used to update node features. In addition, a node feature updating method that simultaneously uses global and local weights is proposed. Multiple comparative experiments on five benchmark datasets demonstrate that the method proposed in this article is optimal and the accuracy and micro-F1 have improved by at least 0.012 on four benchmark datasets.