Given the success of Graph Neural Networks (GNNs) for structure-aware machine learning, many studies have explored their use for text classification, but mostly in specific domains with limited data characteristics. Moreover, some strategies prior to GNNs relied on graph mining and classical machine learning, making it difficult to assess their effectiveness in modern settings. This work extensively investigates graph representation methods for text classification, identifying practical implications and open challenges. We compare different graph construction schemes using a variety of GNN architectures and setups across five datasets, encompassing short and long documents as well as unbalanced scenarios in diverse domains. Two Transformer-based large language models are also included to complement the study. The results show that i) although the effectiveness of graphs depends on the textual input features and domain, simple graph constructions perform better the longer the documents are, ii) graph representations are especially beneficial for longer documents, outperforming Transformer-based models, iii) graph methods are particularly efficient at solving the task.

翻译：鉴于图神经网络（GNN）在结构感知机器学习中取得的成功，许多研究已探索其在文本分类中的应用，但大多局限于数据特征有限的特定领域。此外，部分GNN之前的策略依赖图挖掘和经典机器学习，这使得评估其在现代环境中的有效性变得困难。本研究广泛探讨了用于文本分类的图表示方法，识别了实际应用中的意义和开放挑战。我们使用多种GNN架构和配置，在五个数据集上比较了不同的图构建方案，这些数据集涵盖短文档和长文档，以及不同领域的不平衡场景。为补充研究，还纳入了两种基于Transformer的大型语言模型。结果表明：i）尽管图的有效性取决于文本输入特征和领域，但简单的图构建在文档越长时表现越好；ii）图表示尤其对长文档有利，其性能优于基于Transformer的模型；iii）图方法在解决该任务时特别高效。