The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies
Abstract
We present the nested Chinese restaurant process (nCRP), a stochastic process that assigns probability distributions to ensembles of infinitely deep, infinitely branching trees. We show how this stochastic process can be used as a prior distribution in a Bayesian nonparametric model of document collections. Specifically, 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 finds 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 scientific abstracts from several journals. This model exemplifies a recent trend in statistical machine learning—the use of Bayesian nonparametric methods to infer distributions on flexible data structures.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2010
- Source ID
- 10.1145/1667053.1667056
Entities
People
- David M. Blei
- Michael I. Jordan
- Thomas L. Griffiths
Organizations
- Division of Behavioral and Cognitive Sciences
- National Science Foundation
- Office of Naval Research
- Princeton University
- University of California, Berkeley