Content Modeling Using Latent Permutations
Name
C4A90305d01.pdf
Size
319.25 KB
Format
Adobe PDF
Checksum (MD5)
2be282dfbad5f27fb88ae916b7a132ac
Author(s)
Chen, Harr
Branavan, Satchuthanan R.
Barzilay, Regina
Karger, David R.
Date Issued
October 2009
Journal
Journal of Artificial Intelligence Research
Publisher
AI Access Foundation
Citation
H. Chen, S.R.K. Branavan, R. Barzilay and D. R. Karger (2009) "Content Modeling Using Latent Permutations", Journal of Artificial Intelligence Research. Volume 36, pages 129-163. © 2009 AI Access Foundation.
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
http://dx.doi.org/10.1613/jair.2830
