A new knowledge-enhanced graph convolutional neural network (KEGCN) classification model was proposed aiming at the problem of text classification. In the KEGCN model, firstly a text graph containing word nodes, document nodes, and external entity nodes was constructed on the entire text set. Different similarity calculation methods were used between different types of nodes. After the text graph was constructed, it was input into the two-layer graph convolutional network to learn the representation of the node and classified. The KEGCN model introduced external knowledge to compose the graph, and captured the long-distance discontinuous global semantic information, and was the first work to introduce knowledge information into the graph convolution network for classification tasks. Text classification experiments were conducted on four large-scale real data sets, 20NG, OHSUMED, R52 and R8, and results showed that the classification accuracy of the KEGCN network model was better than that of all baseline models. Results show that integrating knowledge information into the graph convolutional neural network is conducive to learning more accurate text representations and improving the accuracy of text classification.
Keywords：knowledge embedding
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graph convolutional network
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neural network
;
text classification
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natural language processing
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