Text Classification Using Document-Relational Graph Convolutional Networks

Graph Convolutional Networks (GCNs) have received considerable attention in the field of artificial machine intelligence (AMI) and natural language processing research because they can build more sophisticated accompanying graph structures than traditional neural networks for feature engineering. Graph is used as feature in neural network because it is easy to find relations among nodes. In text classification applications, a GCN can create complex and rich relation-based adjacent matrix graphs as features to be trained. The existing methods, on the other hand, only generated adjacent matrix graphs in GCN at the word-document and word-word levels as features. In this paper, we propose a document-relational GCN to achieve superior accuracy in text classification by adding cumulative term frequency-inverse document frequency (TF-IDF) document-document relations as features. The performance of the proposed method is evaluated using five popular benchmark databases. In addition, different hidden nodes and proportions of document-document features are tested to achieve an advantageous outcome.

Automated summary

Graph Convolutional Networks have gained attention in the field of artificial machine intelligence and natural language processing for their ability to create sophisticated graph structures for feature engineering. In this paper, we propose a document-relational GCN that improves text classification accuracy by incorporating document-document relations as features.