Text classification based on graph neural networks with gate mechanism

Current research on graph neural network-based text classification models faces challenges such as dependence on fixed corpora and overlooking hidden information between text sequences.In order to solve the shortcomings of previous studies, we construct a new graphical neural text classification model, Message passing Gated Model (MPGM).This model features a global graph with parameter-shared nodes for each input text, and leverages gate-controlled recurrent units (GRU) to learn local information within the texts while capturing semantic relationships between contexts. Experimental results on several data sets show that the proposed model is superior to other text classification methods based on graph neural networks.