H. Chen, S.R.K. Branavan, R. Barzilay and D. R. Karger (2009) "Content Modeling Using Latent Permutations", Volume 36, pages 129-163

PDF | PostScript | doi:10.1613/jair.2830

We present a novel Bayesian topic model for learning discourse-level document structure. Our model leverages insights from discourse theory to constrain latent topic assignments in a way that reflects the underlying organization of document topics. We propose a global model in which both topic selection and ordering are biased to be similar across a collection of related documents. We show that this space of orderings can be effectively represented using a distribution over permutations called the Generalized Mallows Model. We apply our method to three complementary discourse-level tasks: cross-document alignment, document segmentation, and information ordering. Our experiments show that incorporating our permutation-based model in these applications yields substantial improvements in performance over previously proposed methods.

Click here to return to Volume 36 contents list