Text clustering, as one of the most fundamental challenges in unsupervised learning, aims at grouping semantically similar text segments without relying on human annotations. With the rapid development of deep learning, deep clustering has achieved significant advantages over traditional clustering methods. Despite the effectiveness, most existing deep text clustering methods rely heavily on representations pre-trained in general domains, which may not be the most suitable solution for clustering in specific target domains. To address this issue, we propose CEIL, a novel Classification-Enhanced Iterative Learning framework for short text clustering, which aims at generally promoting the clustering performance by introducing a classification objective to iteratively improve feature representations. In each iteration, we first adopt a language model to retrieve the initial text representations, from which the clustering results are collected using our proposed Category Disentangled Contrastive Clustering (CDCC) algorithm. After strict data filtering and aggregation processes, samples with clean category labels are retrieved, which serve as supervision information to update the language model with the classification objective via a prompt learning approach. Finally, the updated language model with improved representation ability is used to enhance clustering in the next iteration. Extensive experiments demonstrate that the CEIL framework significantly improves the clustering performance over iterations, and is generally effective on various clustering algorithms. Moreover, by incorporating CEIL on CDCC, we achieve the state-of-the-art clustering performance on a wide range of short text clustering benchmarks outperforming other strong baseline methods.

翻译：文本聚类作为无监督学习中最基本的挑战之一，旨在无需人工标注的情况下，将语义相似的文本片段进行分组。随着深度学习的快速发展，深度聚类相比传统聚类方法展现出显著优势。尽管效果显著，但现有大多数深度文本聚类方法严重依赖在通用领域预训练的表示，这可能并非针对特定目标域聚类的最优方案。为解决该问题，我们提出CEIL——一种面向短文本聚类的新型分类增强迭代学习框架，其核心思想是通过引入分类目标来迭代优化特征表示，从而普遍提升聚类性能。在每次迭代中，我们首先采用语言模型获取初始文本表示，并通过提出的类别解耦对比聚类（CDCC）算法从这些表示中收集聚类结果。经过严格的数据过滤与聚合流程后，获得带有清晰类别标签的样本，这些样本通过提示学习方法作为监督信息，以分类目标更新语言模型。最后，利用具有更强表示能力的更新后语言模型来增强下一轮迭代的聚类。大量实验表明，CEIL框架能显著提升迭代过程中的聚类性能，且普遍适用于多种聚类算法。此外，通过将CEIL集成到CDCC中，我们在多项短文本聚类基准测试中取得了最先进的聚类性能，优于其他强基线方法。