Global models of document structure using latent permutations
Author(s)Chen, Harr; Branavan, Satchuthanan R.; Barzilay, Regina; Karger, David R.
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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 elegantly represented using a distribution over permutations called the generalized Mallows model. Our structure-aware approach substantially outperforms alternative approaches for cross-document comparison and single-document segmentation.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Proceedings of Human Language Technologies: the 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Association for Computational Linguistics
Chen, Harr, S.R.K. Branavan, Regina Barzilay, and David R. Karger (2009). "Global models of document structure using latent permutations." Proceedings of Human Language Technologies: the 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics (Morristown, N.J.: Association for Computational Linguistics): 371-379. © 2009 Association for Computing Machinery.
Final published version
algorithms, design, experimentation, languages, measurement, performance