A Manifold-Aware Topic Modeling Approach via Rank-Based Prototypes

arXiv:2609.29630 · cs.LG, cs.AI, cs.CL, cs.IR · Submitted 2026-08-29 · Read on arXiv

cs.LG, cs.AI, cs.CL, cs.IR

Submitted: 2026-08-29

Updated: 2026-08-29

Code: https://github.com/thcastilho/maretopic

Project page: https://maartengr.github.io/BERTopic/index.html#attributes

Terminology

• Clustering — 30× in this paper · explained in 2 episodes
• Embeddings — 14× in this paper · explained in 2 episodes
• New York Times — 7× in this paper
• Topic Diversity — 7× in this paper
• Robustness — 6× in this paper · explained in 5 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Cosine Similarity — 4× in this paper · explained in 3 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Anisotropy — 3× in this paper · explained in 3 episodes
• Interpretability — 3× in this paper · explained in 15 episodes
• Maximal Marginal Relevance — 3× in this paper
• Redundancy — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Optimal Transport — 2× in this paper · explained in 5 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Query Performance Prediction — 1× in this paper
• Transferability — 1× in this paper · explained in 4 episodes

Sources

• Top2Vec: Distributed Representations of Topics
• BERTopic: Neural topic modeling with a class-based TF-IDF procedure
• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

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