A Nested HDP for Hierarchical Topic Models
Abstract
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.
Cite
Text
Paisley et al. "A Nested HDP for Hierarchical Topic Models." International Conference on Learning Representations, 2013.Markdown
[Paisley et al. "A Nested HDP for Hierarchical Topic Models." International Conference on Learning Representations, 2013.](https://mlanthology.org/iclr/2013/paisley2013iclr-nested/)BibTeX
@inproceedings{paisley2013iclr-nested,
title = {{A Nested HDP for Hierarchical Topic Models}},
author = {Paisley, John W. and Wang, Chong and Blei, David M. and Jordan, Michael I.},
booktitle = {International Conference on Learning Representations},
year = {2013},
url = {https://mlanthology.org/iclr/2013/paisley2013iclr-nested/}
}