Development of a graph neural network for processing text data

Currently, one of the main directions of information technology development is graph-based modeling of complex data structures and machine learning approaches based on graph representations. The article deals with graph modeling of text data using neural networks. The aim of the paper is to develop a graph neural network for classification and clustering of texts based on semantic content. Texts are represented as graphs, where vertices are concepts and edges are links between them. Public text corpora in Russian and English were used. A new approach to analyzing text data was proposed based on their representation in the form of oriented weighted graphs and processing by graph neural networks. The graphs were processed by a neural network with three layers of graph convolutions. The obtained results show an accuracy of more than 90% for topic group classification and text clustering, outperforming RNN, CNN and doc2vec methods.