A Nested HDP for Hierarchical Topic Models
Machine Learning
2013-01-17 v1
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
@article{arxiv.1301.3570,
title = {A Nested HDP for Hierarchical Topic Models},
author = {John Paisley and Chong Wang and David Blei and Michael I. Jordan},
journal= {arXiv preprint arXiv:1301.3570},
year = {2013}
}
Comments
Submitted to the workshop track of the International Conference on Learning Representations 2013. It is a short version of a longer paper