Abstract
Short text classification (STC) remains a challenging task due to the scarcity of contextual information and labeled data. However, existing approaches have pre-dominantly focused on English because most benchmark datasets for the STC are primarily available in English. Consequently, existing methods seldom incorporate the linguistic and structural characteristics of Korean, such as its agglutinative morphology and flexible word order. To address these limitations, we propose LIGRAM, a hierarchical heterogeneous graph model for Korean short-text classification. The proposed model constructs sub-graphs at the morpheme, part-of-speech, and named-entity levels and hierarchically integrates them to compensate for the limited contextual information in short texts while precisely capturing the grammatical and semantic dependencies inherent in Korean. In addition, we apply Semantics-aware Contrastive Learning (SemCon) to reflect semantic similarity across documents, enabling the model to establish clearer decision boundaries even in short texts where class distinctions are often ambiguous. We evaluate LIGRAM on four Korean short-text datasets, where it consistently outperforms existing baseline models. These outcomes validate that integrating language-specific graph representations with SemCon provides an effective solution for short text classification in agglutinative languages such as Korean.
Abstract (translated)
短文本分类（STC）任务由于缺乏上下文信息和标注数据而仍然具有挑战性。然而，现有的方法主要集中在英语上，因为大多数基准数据集都是以英语为主的。因此，现有方法很少考虑韩语的语言和结构特征，例如其黏着形态和灵活的词序特点。为了解决这些问题，我们提出了LIGRAM模型，这是一种用于韩语短文本分类的层次异构图模型。该模型构建了音素级、词性级和命名实体级别的子图，并分层地整合它们以弥补短文本中上下文信息不足的问题，同时精确捕捉到韩语内在的语法和语义依存关系。 此外，我们还应用了基于语义感知对比学习（SemCon）的方法来反映文档之间的语义相似性，在短文本中即使类别区分模糊的情况下也能帮助模型建立更清晰的决策边界。我们在四个韩语短文本数据集上对LIGRAM进行了评估，结果表明它在所有情况下都优于现有的基线模型。这些成果验证了将特定语言图表示与SemCon相结合可以为像韩语这样的黏着语系提供有效的短文本分类解决方案。
URL
https://arxiv.org/abs/2603.03652
