  Advances in topic modeling have yielded effective methods for characterizing
the latent semantics of textual data. However, applying standard topic modeling
approaches to sentence-level tasks introduces a number of challenges. In this
paper, we adapt the approach of latent-Dirichlet allocation to include an
additional layer for incorporating information about the sentence boundaries in
documents. We show that the addition of this minimal information of document
structure improves the perplexity results of a trained model.
