A Text Classification Model Based on GCN and BiGRU Fusion.

A text classification model with the fusion of graph convolutional neural network (GCN) and bi-directional gated recurrent unit (BiGRU) is designed to address the lack of ability of simple neural networks to capture the contextual semantics of text, extract spatial feature information of text and nonlinear complex semantic relations. First, the text is preprocessed and text vectorization is performed by Word2Vec; then, the graph convolutional neural network and bi-directional gated recurrent unit are fused to form a hybrid model so that it can extract complex semantic relations and spatial feature information of the text; finally, the classification is performed by a softmax classifier. Experiments are conducted on a publicly available dataset, and the results demonstrate that the model can effectively improve the performance of text classification.