Hierarchical topic modeling with nested hierarchical Dirichlet process

作     者：Yi-qun DING Shan-ping LI Zhen ZHANG Bin SHEN 

作者机构：School of Computer Science and Technology Zhejiang University Hangzhou 310027 China State Street Hangzhou Hangzhou 310000 China 

出 版 物：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 (浙江大学学报（英文版）A辑（应用物理与工程）)

年 卷 期：2009年第10卷第6期

页      面：858-867页

核心收录：

学科分类：07[理学] 070205[理学-凝聚态物理] 08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 0702[理学-物理学] 

基　　金：Project (No. 60773180) supported by the National Natural Science Foundation of China 

主　　题：Topic modeling Natural language processing Chinese restaurant process Hierarchical Dirichlet process Markovchain Monte Carlo Nonparametric Bayesian statistics 

摘      要：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 nonpara-metric 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 rela-tionships compared to the hierarchical latent Dirichlet allocation model.