Given the success of Graph Neural Networks (GNNs) for structure-aware machine learning, numerous studies have explored their application to text classification, as an alternative to traditional feature representation models. However, most studies considered just a specific domain and validated on data with particular characteristics. This work presents an extensive empirical investigation of graph-based text representation methods proposed for text classification, identifying practical implications and open challenges in the field. We compare several GNN architectures as well as BERT across five datasets, encompassing short and also long documents. The results show that: i) graph performance is highly related to the textual input features and domain, ii) despite its outstanding performance, BERT has difficulties converging when dealing with short texts, iii) graph methods are particularly beneficial for longer documents.

本研究对基于图的文本表示方法在文本分类中的应用进行了广泛的实证研究，探索了其实际影响和开放挑战。在短文档和长文档数据集上比较了几个GNN架构和BERT的性能，并发现文本输入特征和领域高度相关，尽管BERT表现出色，但在处理短文本时存在收敛困难，而图方法特别有利于处理较长文本。

如何使用基于图神经网络的最佳文本表示法进行文本分类: 连接数据点?