Details

Autor(en) / Beteiligte

• Ding, Yi-qun
• Li, Shan-ping
• Zhang, Zhen
• Shen, Bin

Titel

Hierarchical topic modeling with nested hierarchical Dirichlet process

Ist Teil von

• Journal of Zhejiang University. A. Science, 2009-06, Vol.10 (6), p.858-867

Ort / Verlag

Hangzhou: Zhejiang University Press

Erscheinungsjahr

2009

Quelle

SpringerLink Journals - AutoHoldings

Beschreibungen/Notizen

• This paper deals with the statistical modeling of latent topic hierarchies in text corpora. The height of the topic tree is assumed as fixed, while the number of topics on each level as unknown a priori and to be inferred from data. Taking a nonparametric Bayesian approach to this problem, we propose a new probabilistic generative model based on the nested hierarchical Dirichlet process （nHDP） and present a Markov chain Monte Carlo sampling algorithm for the inference of the topic tree structure as well as the word distribution of each topic and topic distribution of each document. Our theoretical analysis and experiment results show that this model can produce a more compact hierarchical topic structure and captures more fine-grained topic relationships compared to the hierarchical latent Dirichlet allocation model.