vMF Sentence LDA: A Spherical Topic Model over Sentence Embeddings
Latent Dirichlet Allocation (LDA) and models derived from it remain widely used topic models. LDA observes each document as a bag-of-words and models each topic by a categorical distribution over the vocabulary, so that it uses neither the internal structure of the document nor the similarity in meaning between words. Earlier work has responded to this limitation in two ways: many models have introduced embeddings, and some have assigned topics to sentences rather than to words. Their combination, a topic model that observes sentence embeddings, remains little explored. We propose vMF Sentence LDA (vSLDA), which keeps the admixture structure of LDA, observes each sentence as its L2-normal...
| Authors | Ryotaro Kobayashi, Yuri Murayama, Kiyoshi Izumi |
|---|---|
| Primary category | cs.CL |
| Published | 4 October 2026 |
| Last updated at source | 4 October 2026 |
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