An Adaptation of Topic Modeling to Sentences
This work addresses a specific problem in natural language processing for researchers and practitioners, but it is incremental as it builds on existing methods with a minor adaptation.
The paper tackled the challenge of applying topic modeling to sentence-level tasks by adapting latent-Dirichlet allocation to incorporate sentence boundary information, resulting in improved perplexity scores.
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.