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.
Meanwhile, this method mainly extracts node features, but neglects the
use of edge features between nodes. To solve these problems, a new text
classification method, graph convolutional network using positions and
edges (PEGCN), 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%.