基于增量概念生成的终身主题模型，可动态构建层次化主题结构。
CobwebTM: Probabilistic Concept Formation for Lifelong and Hierarchical Topic Modeling
- 通过改进Cobweb算法实现文档嵌入的在线语义分层
- 无需预设主题数，长期保持主题稳定且相干性高
- 适合持续学习场景，尤其适用于流式文本数据
主题建模旨在以最少监督揭示文本语料中的潜在语义结构。神经方法表现强劲但需大量调参，且因灾难性遗忘和固定容量难以适应终身学习；经典概率模型则缺乏灵活性与对流式数据的适应能力。我们提出CobwebTM，一种低参数、终身学习的层次化主题模型，基于增量概率概念形成。通过将Cobweb算法适配连续文档嵌入，该模型可在线构建语义层次，实现无监督主题发现、动态主题创建及层次组织，无需预先设定主题数量。在多种数据集上，CobwebTM均表现出强主题连贯性、时间稳定性与高质量层次结构，表明结合增量符号概念形成与预训练表示是高效的主题建模路径。
原文摘要 · Abstract (English)
Topic modeling seeks to uncover latent semantic structure in text corpora with minimal supervision. Neural approaches achieve strong performance but require extensive tuning and struggle with lifelong learning due to catastrophic forgetting and fixed capacity, while classical probabilistic models lack flexibility and adaptability to streaming data. We introduce CobwebTM, a low-parameter lifelong hierarchical topic model based on incremental probabilistic concept formation. By adapting the Cobweb algorithm to continuous document embeddings, CobwebTM constructs semantic hierarchies online, enabling unsupervised topic discovery, dynamic topic creation, and hierarchical organization without predefining the number of topics. Across diverse datasets, CobwebTM achieves strong topic coherence, stable topics over time, and high-quality hierarchies, demonstrating that incremental symbolic concept formation combined with pretrained representations is an efficient approach to topic modeling.
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