June 1, 2010
Not-so-latent dirichlet allocation: collapsed Gibbs sampling using human judgments
Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)
Probabilistic topic models are a popular tool for the unsupervised analysis of text, providing both a predictive model of future text and a latent topic representation of the corpus. Recent studies have found that while there are suggestive connections between topic models and the way humans interpret data, these two often disagree.
By: Jonathan Chang