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arXiv · 0710.0845

The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies

David M. Blei·Thomas L. Griffiths·Michael I. Jordan
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Abstract

We present the nested Chinese restaurant process (nCRP), a stochastic process which assigns probability distributions to 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.

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David M. Blei, Thomas L. Griffiths, Michael I. Jordan. 2009-08-27. The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies. https://arxiv.org/abs/0710.0845

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