Text classification is a foundational natural language processing task. Many models have transformed text data into innovative graph structures and employed graph neural networks (GNNs) to learn representations for classification. However, graphs constructed based on artificial rules may contain redundant connections, which can introduce noise. Additionally, graph neural network-based (GNN-based) models are ineffective in learning word order. To cope with these difficulties, we propose graph affine Transformer with a symmetric adaptation strategy (GATSAS) for text classification. After graph c...
