Research Publications
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| Twitter: the world of 140 characters poses serious challenges to the efficacy of topic models on short, messy text. While topic models such as Latent Dirichlet Allocation (LDA) have a long history of successful application to news articles and ... We present a new hierarchical Bayesian model for unsupervised topic segmentation.~This new model integrates a point-wise boundary sampling algorithm used in Bayesian segmentation into a structured topic model that can capture a simple hierarchical ... We extend the Bayesian skill rating system of TrueSkill to accommodate score-based match outcomes. TrueSkill has proven to be a very effective algorithm for matchmaking --- the process of pairing competitors based on similar skill-level --- in ... Topic models are increasingly being used for text analysis tasks, often times replacing earlier semantic techniques such as latent seman- tic analysis. In this paper, we develop a novel adaptive topic model with the ability to adapt topics from both ... We develop dependent hierarchical normalized random measures and apply them to dynamic topic modeling. The dependency arises via {\em superposition}, {\em subsampling} and {\em point transition} on the underlying Poisson processes of these measures. ... The two parameter Poisson-Dirichlet Process (PDP), a generalisation of the Dirichlet Process, is increasingly being used for probabilistic modelling in discrete areas such as language technology, bioinformatics, and image analysis. There is a rich ... Topic models have the potential to improve search and browsing by extracting useful semantic themes from web pages and other text documents. When learned topics are coherent and interpretable, they can be valuable for faceted browsing, results set ... Hierarchical modeling and reasoning are fundamental in ma- chine intelligence, and for this the two-parameter Poisson-Dirichlet Pro- cess (PDP) plays an important role. The most popular MCMC sampling algorithm for the hierarchical PDP and hierarchical ... Understanding how topics within a document evolve over its structure is an interesting and important problem. In this paper, we address this problem by presenting a novel variant of Latent Dirichlet Allocation (LDA): Sequential LDA (SeqLDA). This ... We extend Latent Dirichlet Allocation (LDA) by explicitly allowing for the encoding of side information in the distribution over words. This results in a variety of new capabilities, such as improved estimates for infrequently occurring words, as well as ... |