Text Classification Method Based on Information Fusion of Dual-graph Neural Network

Abstract

Graph neural networks are recently applied in text classification tasks.Compared with graph convolution network,the text level graph neural network model based on message passing(MP-GNN) has the advantages of low memory usage and supporting online testing.However,MP-GNN model only builds a lexical graph using the word co-occurrence information, and the obtained information lacks diversity.To address this problem,a text classification method based on information fusion of dual-graph neural network is proposed.Besides preserving the original lexical graph built in MP-GNN,this method constructes the semantic graph based on the cosine similarity between pairs of words,and controls the sparsity of the graph through a threshold,which makes more effective use of the multi-directional semantic information of the text.In addition,the ability of direct fusion and attention mechanism fusion are tested to fuse the text representation learned on lexical graph and semantic graph.Experimental results on 12 datasets(R8,R52 and other datasets commonly used for text classification) show that the proposed model demonstrates an obvious improvement on performance of text classification compared with the SOTA(state-of-the-art) methods TextLevelGNN,TextING and MPAD.

Keywords

• text classification|graph neural network|semantic information|information fusion|attention mechanism|natural language processing