Text Classification Based on Attention Gated Graph Neural Network

Abstract

To address the problem that the existing text classification work usually ignores the semantic interaction between words when generating text representation,this paper proposes a novel text classification model based on attention gated graph neural network.It makes effective use of the semantic features of words and improves the accuracy of text classification based on the adequate semantic interaction.Firstly,each input text is converted to a single graph-structured data and the semantic features of word nodes are extracted.Secondly,attention gated graph neural network is used to interact and update the semantic features of word nodes.In addition,the attention-based text pooling module is used to extract the word nodes with discriminative semantic features to construct text graph representation.Finally,effective text classification is implemented based on the text graph representation.Experimental results show that the proposed method achieves an accuracy of 70.83%,98.18%,94.72% and 80.03% on Ohsumed,R8,R52 and MR datasets,respectively,and outperforms existing methods.

Keywords

• deep learning|text classification|graph neural network|attention mechanism