Modeling the evolution of topics with time is of great value in automatic summarization and analysis of large document collections. In this work, we propose a new probabilistic gr...
Ramesh Nallapati, Susan Ditmore, John D. Lafferty,...
We study the interaction between global and local techniques in data mining. Specifically, we study the collections of frequent sets in clusters produced by a probabilistic clust...
We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic pro...
Mark Steyvers, Padhraic Smyth, Michal Rosen-Zvi, T...
Mixture models form one of the most widely used classes of generative models for describing structured and clustered data. In this paper we develop a new approach for the analysis...
This paper addresses the challenging problem of learning from multiple annotators whose labeling accuracy (reliability) differs and varies over time. We propose a framework based ...