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We study the applications of hierarchical topic models to represent the content of website summaries. We concentrate on the DMOZ collection of Web extracts and propose a novel Tree Labeled LDA… (More)
We study the problem of topic modeling in continuous social media streams and propose a new generative probabilistic model called Hash-Based Stream LDA (HS-LDA), which is a generalization of the… (More)
Epidemiologists typically collect narrative descriptions of occupational histories because these are less prone than self-reported exposures to recall bias of exposure to a specific hazard. However,… (More)
We investigated automatic approaches for clustering data that describes occupations related to hazardous airborne exposure (beryllium). The regulatory compliance data from Occupational Safety and… (More)
Topic modeling approaches, such as Latent Dirichlet Allocation (LDA) and Hierarchical LDA (hLDA) have been used extensively to discover topics in various corpora. Unfortunately, these approaches do… (More)
Advisor: Xiaohua (Tony) Hu, Ph.D. Co-Advisor: Yuan An, Ph.D. College of Computing and Informatics Doctor of Philosophy