Text Classification Using a Graph Based on Relationships Between Documents

Hiromu Nakajima, Minoru Sasaki

Abstract: Text classification, which determines the genre of a document based on cues such as the cooccurrence of words and their frequency of occurrence, has been studied in various approaches to date. Conventional text classification methods using graph-structured data express relationships between words and between words and documents in the form of weights of edges between each node. Then, the graph is input to a graph neural network for learning. However, conventional methods do not represent the relationship between documents on the graph, and thus cannot directly consider the relationship between documents. Therefore, we propose a text classification method using the graph considers the relationships among documents. This method directly expresses the relationship between documents by adding the similarity of documents as weights of edges between document nodes to the graph of the conventional method. The constructed graph is then input to a graph convolutional neural network for learning. We conducted experiments using five English corpus (20NG, R52, R8, Ohsumed, and MR) to evaluate proposed method. The results show that the proposed method improves accuracy compared to the conventional method and that the use of relationships among document nodes is effective. Experimental results also show that the proposed method is particularly effective on datasets with relatively long documents.