Text Graph Transformer for Document Classification
Text classification is a fundamental problem in natural language processing. Recent studies applied graph neural network (GNN) techniques to capture global word co occurrence in a corpus. However, previous works are not scalable to large sized corpus and ignore the heterogeneity of the text graph. To address these problems, we introduce a novel Transformer based heterogeneous graph neural network, namely Text Graph Transformer (TG Transformer) Our model learns effective node representations by capturing structure and heterogeneity from the text graph. We propose a mini batch text graph sampling method that significantly reduces computing and memory costs to handle large sized corpus. Extensive experiments have been conducted on several benchmark datasets, and the results demonstrate that TG Transformer outperforms state of the art approaches on text classification task.
EMNLP