We propose a new hierarchical Bayesian n-gram model of natural languages. Our model makes use of a generalization of the commonly used Dirichlet distributions called Pitman-Yor pr...
We address the problem of learning topic hierarchies from data. The model selection problem in this domain is daunting—which of the large collection of possible trees to use? We...
David M. Blei, Thomas L. Griffiths, Michael I. Jor...
Probabilistic retrieval models usually rank documents based on a scalar quantity. However, such models lack any estimate for the uncertainty associated with a document’s rank. Fu...
Jianhan Zhu, Jun Wang, Michael J. Taylor, Ingemar ...
The aim of query-based sampling is to obtain a sufficient, representative sample of an underlying (text) collection. Current measures for assessing sample quality are too coarse gr...
The optimal settings of retrieval parameters often depend on both the document collection and the query, and are usually found through empirical tuning. In this paper, we propose ...