Text classification based on subgraph fusion graph convolutional network

Text classification has always been an important task in natural language processing (NLP). In recent years, by mapping text data to graph structures, researchers can perform more complex data analysis to improve classification accuracy. However, existing methods have two major limitations: First, they rely on constructing corpus-level graph structures and use fixed-weighted edges, which limits the expressibility of edges; Secondly, these methods usually create edges only by capturing the co-occurrence relationship between words, unable to capture the deep semantic information between words, and ignore the context information to a certain extent. To overcome these limitations, we propose a Text Classification Based on Subgraph Fusion Graph Convolutional Network (TF-GCN) text classification model. The model divides the training set into k subgraphs according to class, and constructs the word co-occurrence graph of each subgraph and the semantic relation graph of subgraph to capture the context information. By fusing these subgraphs and applying the learning weight adaptation method, a more fine-grained subgraph feature representation is realized. Eventually, these subgraphs are combined into one comprehensive large graph for representation. Experiments on four benchmark datasets show that our proposed text classification method has advantages over existing techniques.