Hybrid Augmentation of Text Feature and Graph Node for Graph Convolutional Networks Text Classification
The work will improve the structure on the basis of the BertGCN model,not only using a new algorithm to construct the edges of the graph,but also combining a hybrid enhancement of text features and graph nodes.The method not only has some optimization in the edge structure,but also makes fuller use of the extended semantic information of the text in the form of text feature enhancement and graph-enhanced nodes,while retaining the original text features.Four public datasets,R8,R52,Ohsumed and MR which are commonly used,are used to verify the effectiveness of this method.The experimental results show that compared with the BertGCN model and other baselines,the accuracy evaluation metric of the method on the four text classification data sets has been improved to varying degrees.
NETL
NSTL
万方数据
维普
