Journal
JOURNAL OF THE ACM
Volume 57, Issue 2, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/1667053.1667056
Keywords
Algorithms; Experimentation; Bayesian nonparametric statistics; unsupervised learning
Categories
Funding
- ONR [175-6343]
- NSF CAREER [0745520]
- Google and Microsoft Research
- NSF [BCS-0631518]
- DARPA CALO
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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.
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