# A Nested HDP for Hierarchical Topic Models

arXiv: https://arxiv.org/abs/1301.3570 · HTML: https://mlsanity.com/paper/1301.3570/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2013.

## Built on by (most cited first, 2 of 2)

- [On the Affinity, Rationality, and Diversity of Hierarchical Topic Modeling](https://mlsanity.com/paper/2401.14113/) (2024)
- [HyHTM: Hyperbolic Geometry based Hierarchical Topic Models](https://mlsanity.com/paper/2305.09258/) (2023)

## Abstract (arXiv)

We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling. The nHDP is a generalization of the nested Chinese restaurant process (nCRP) that allows each word to follow its own path to a topic node according to a document-specific distribution on a shared tree. This alleviates the rigid, single-path formulation of the nCRP, allowing a document to more easily express thematic borrowings as a random effect. We demonstrate our algorithm on 1.8 million documents from The New York Times.