Text classification based on PEGCN: Graph convolution classification using location information and edge features

The purpose of text classification is to label the text with known labels. In recent years, the method based on graph neural network (GNN) has achieved good results. However, the existing methods based on GNN only regard the text as the set of co-occurring words, without considering the position information of each word in the statement. At the same time, the method mainly extracts the node features in the graph, and the edge features between the nodes are not used enough. To solve these problems, a new text classification method, graph convolutional network using positions and edges, is proposed. In the word embedding section, a positional encoding input representation is employed to enable the neural network to learn the relative positional information among words. Meanwhile, the dimension of the adjacency matrix is increased to extract the multi-dimensional edge features. Through experiments on multiple text classification datasets, the proposed method is shown to be superior to the traditional text classification method, and has achieved a maximum improvement of more than 4%.

Automated summary

This article introduces a new text classification method using positional and edge graph convolutional networks. By adding positional encoding input representation and extracting multi-dimensional edge features, this method solves the problem of insufficient utilization of position information and edge features in existing methods, and achieves good classification results on multiple datasets.