On dual decomposition and linear programming relaxations for natural language processing
Author(s)Rush, Alexander Matthew; Sontag, David Alexander; Collins, Michael; Jaakkola, Tommi S.
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This paper introduces dual decomposition as a framework for deriving inference algorithms for NLP problems. The approach relies on standard dynamic-programming algorithms as oracle solvers for sub-problems, together with a simple method for forcing agreement between the different oracles. The approach provably solves a linear programming (LP) relaxation of the global inference problem. It leads to algorithms that are simple, in that they use existing decoding algorithms; efficient, in that they avoid exact algorithms for the full model; and often exact, in that empirically they often recover the correct solution in spite of using an LP relaxation. We give experimental results on two problems: 1) the combination of two lexicalized parsing models; and 2) the combination of a lexicalized parsing model and a trigram part-of-speech tagger.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Conference on Empirical Methods in Natural Language Processing 2010, Proceedings
Association for Computational Linguistics
Rush, Alexander M. et al. “On dual decomposition and linear programming relaxations for natural language processing.” Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. Cambridge, Massachusetts: Association for Computational Linguistics, 2010. 1-11. c2010 Association for Computational Linguistics.
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