Abstract
Text classification plays an important role in various downstream text-related tasks, such as sentiment analysis, fake news detection, and public opinion analysis. Recently, text classification based on Graph Neural Networks (GNNs) has made significant progress due to their strong capabilities of structural relationship learning. However, these approaches still face two major limitations. First, these approaches fail to fully consider the diverse structural information across word pairs, e.g., co-occurrence, syntax, and semantics. Furthermore, they neglect sequence information in the text graph structure information learning module and can not classify texts with new words and relations. In this paper, we propose a Novel Graph-Sequence Learning Model for Inductive Text Classification (TextGSL) to address the previously mentioned issues. More specifically, we construct a single text-level graph for all words in each text and establish different edge types based on the diverse relationships between word pairs. Building upon this, we design an adaptive multi-edge message-passing paradigm to aggregate diverse structural information between word pairs. Additionally, sequential information among text data can be captured by the proposed TextGSL through the incorporation of Transformer layers. Therefore, TextGSL can learn more discriminative text representations. TextGSL has been comprehensively compared with several strong baselines. The experimental results on diverse benchmarking datasets demonstrate that TextGSL outperforms these baselines in terms of accuracy.
Abstract (translated)
文本分类在诸如情感分析、假新闻检测和公众舆论分析等各种下游与文本相关的任务中扮演着重要角色。最近，基于图神经网络（Graph Neural Networks, GNNs）的文本分类方法由于其强大的结构关系学习能力取得了显著进展。然而，这些方法仍然面临着两个主要限制：首先，它们未能充分考虑词汇对之间多样化的结构性信息，如共现、句法和语义；其次，它们忽视了在文本图结构信息学习模块中的序列信息，并且无法分类含有新词和关系的文本。 为了应对上述问题，在本文中我们提出了一种新颖的图-序列学习模型（TextGSL），用于归纳式文本分类。具体来说，我们在每个文本级别上构建了一个包含所有词汇的单一文本级图，并根据词汇对之间的多样化关系建立了不同类型的边。在此基础上，我们设计了自适应多边消息传递范例来聚合词汇对之间多样化的结构信息。此外，通过引入Transformer层，TextGSL可以捕捉到数据中的序列信息。因此，TextGSL能够学习更具区分性的文本表示。 我们在多种强大的基准线上全面比较了TextGSL的表现，并在一系列不同的基准测试数据集上展示了实验结果：结果显示，TextGSL在准确性方面优于这些基线模型。
URL
https://arxiv.org/abs/2512.20097
