Maximal-Semantics-Augmented BertGCN for Text Classification

Text classification is an important research work in the field of natural language processing (NLP), and many methods of machine learning and deep learning are widely used in this work. In this paper, we propose a method namely Maximal-Semantics-Augmented BertGCN (MSABertGCN) based on BertGCN that further improves the results of text categorization tasks. In this work, the extended semantic information of text is utilized more effectively by means of text semantic enhancement and graph nodes enhancement while preserving the original text features. Four datasets commonly used in the fields of text classification, namely R8, R52, Ohsumed and MR, were used to verify the validity of the method we proposed. Experimental results show that compared with BertGCN and other baselines, the proposed method MSABertGCN has varying degrees of improvement in the accuracy with respect to the R8, R52, Ohsumed and MR datasets.