We present the nested Chinese restaurant process (nCRP), a stochastic process that assigns probability distributions to ensembles of inÞnitely deep, inÞnitely branching trees. We show how this stochastic process can be used as a prior distribution in a Bayesian nonparametric model of document collections. SpeciÞcally, we present an application to information retrieval in which documents are modeled as paths down a random tree, and the preferential attachment dynamics of the nCRP leads to clustering of documents according to sharing of topics at multiple levels of abstraction. Given a corpus of documents, a posterior inference algorithm Þnds an approximation to a posterior distribution over trees, topics and allocations of words to levels of the tree. We demonstrate this algorithm on collections of scientiÞc abstracts from several journals. This model exempliÞes a recent trend in statistical machine learning-the use of Bayesian nonparametric methods to infer distributions on ßexible data structures.
The nested Chinese restaurant process (nCRP) REF models topic hierarchies by introducing a nested Chinese restaurant process prior on a tree.
The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies
JACM
Mathematics Computer Science