Major contributions
The use of a graph convolutional network using positions and edges (PEGCN) model is proposed to address the aforementioned problems. Firstly, aiming at the problem whereby the traditional GNN ignores the relative position relation between words, the position information encoding is added into the word embedding part, so that the network can learn the position information between words. Secondly, in view of the insufficient use of edge features in GNN, the adjacency matrix is proposed to raise and normalize to extract multi-dimensional continuous features of edges. Finally, combining the advantages of the large-scale pre-training model, it can be proved that using the large-scale pre-training model is beneficial to the transduction learning through experiments. The key contribution of the work can be divided into the following parts:
(1) In this paper, we propose the PEGCN model, which effectively addresses the issue of disregarding text positional information in graph neural networks by utilizing input representations with positional information in the word embedding section; (2) The new model can contain multidimensional positive edge features, which overcomes the limitation that the traditional GNN can only process one-dimensional edge features and makes full use of the features of nodes and edges in the graph;