A comparative study of transformer-based embeddings for topic coherence 事件

A comparative study of transformer-based embeddings for topic coherence arXiv:2605.28832v1 Announce Type: new Abstract: Topic modeling is a branch of Natural Language Processing (NLP) that aims to organize large collections of texts into coherent groups according to word co-occurrence patterns, with Latent Dirichlet Allocation (LDA) remaining one of the most widely used and interpretable probabilistic approaches. Recent advances in NLP, particularly transformer-based language models, offer impr