Maximal-Semantics-Augmented BertGCN for Text Classification

Abstract

. Text classification is an important research work in the fields 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 which named Maximal-Semantics-Augmented BertGCN 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 named 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 which named MSABertGCN has varying degrees of improvement in the accuracy of R8, R52, Ohsumed and MR datasets.