In this paper,a nonparametric Bayesian graph topic model(GTM)based on hierarchical Dirichlet process(HDP)is proposed.The HDP makes the number of topics selected flexibly,which breaks the limitation that the number of ...In this paper,a nonparametric Bayesian graph topic model(GTM)based on hierarchical Dirichlet process(HDP)is proposed.The HDP makes the number of topics selected flexibly,which breaks the limitation that the number of topics need to be given in advance.Moreover,theGTMreleases the assumption of‘bag of words’and considers the graph structure of the text.The combination of HDP and GTM takes advantage of both which is named as HDP–GTM.The variational inference algorithm is used for the posterior inference and the convergence of the algorithm is analysed.We apply the proposed model in text categorisation,comparing to three related topic models,latent Dirichlet allocation(LDA),GTM and HDP.展开更多
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 infe...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.展开更多
基金supported by NSFC under grant No.71371074the 111 Project under No.B14019.
文摘In this paper,a nonparametric Bayesian graph topic model(GTM)based on hierarchical Dirichlet process(HDP)is proposed.The HDP makes the number of topics selected flexibly,which breaks the limitation that the number of topics need to be given in advance.Moreover,theGTMreleases the assumption of‘bag of words’and considers the graph structure of the text.The combination of HDP and GTM takes advantage of both which is named as HDP–GTM.The variational inference algorithm is used for the posterior inference and the convergence of the algorithm is analysed.We apply the proposed model in text categorisation,comparing to three related topic models,latent Dirichlet allocation(LDA),GTM and HDP.
基金Project (No. 60773180) supported by the National Natural Science Foundation of China
文摘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.
