# On a Topic Model for Sentences

arXiv: https://arxiv.org/abs/1606.00253 · HTML: https://mlsanity.com/paper/1606.00253/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2016.

## Built on by (most cited first, 4 of 4)

- [Neural Attention-Aware Hierarchical Topic Model](https://mlsanity.com/paper/2110.07161/) (2021)
- [Topic Modeling with Fine-tuning LLMs and Bag of Sentences](https://mlsanity.com/paper/2408.03099/) (2024)
- [Efficient and Flexible Topic Modeling using Pretrained Embeddings and Bag of Sentences](https://mlsanity.com/paper/2302.03106/) (2023)
- [Bilingual Topic Models for Comparable Corpora](https://mlsanity.com/paper/2111.15278/) (2021)

## Abstract (arXiv)

Probabilistic topic models are generative models that describe the content of documents by discovering the latent topics underlying them. However, the structure of the textual input, and for instance the grouping of words in coherent text spans such as sentences, contains much information which is generally lost with these models. In this paper, we propose sentenceLDA, an extension of LDA whose goal is to overcome this limitation by incorporating the structure of the text in the generative and inference processes. We illustrate the advantages of sentenceLDA by comparing it with LDA using both intrinsic (perplexity) and extrinsic (text classification) evaluation tasks on different text collections.