Text classification is an important task in natural language processing.The method of text classification on graph neural network has become a mainstream method since it can model the interactions among texts.However,most of the existing graph-based classification methods rely on real labels,which are difficult to captain.A deep document clustering model based on graph generalization convolutional neural network(GGCN-DDC)is proposed,which can realize unsupervised text classification while learning text representation.Firstly,the documents are modeled as a text graph.Then generalized convolution layer is used to learn the more distinguishable feature representations of words and the document representations.Finally,The learning algorithm of parameters is constrained by document clustering and reconstructing document graph.Experiments on three benchmark datasets show that GGCN-DDC outperforms other benchmark algorithms on several measures.
维普
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