Papers with unsupervised topic discovery

1 papers
CobwebTM: Probabilistic Concept Formation for Lifelong and Hierarchical Topic Modeling (2026.findings-acl)

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Challenge: Topic modeling seeks to uncover latent semantic structure in text corpora with minimal supervision.
Approach: They propose a lifelong hierarchical topic model based on incremental probabilistic concept formation that constructs semantic hierarchies online without predefining the number of topics.
Outcome: The proposed model achieves strong topic coherence, stable topics over time, and high-quality hierarchies without predefining the number of topics.

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