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|a Rush, Alexander Matthew
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|a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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|a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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|a Jaakkola, Tommi S.
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|a Rush, Alexander Matthew
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|a Sontag, David Alexander
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|a Collins, Michael
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|a Jaakkola, Tommi S.
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|a Sontag, David Alexander
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|a Collins, Michael
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|a Jaakkola, Tommi S.
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|a On dual decomposition and linear programming relaxations for natural language processing
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|b Association for Computational Linguistics,
|c 2011-05-18T20:44:45Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/62836
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|a 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.
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|a United States. Defense Advanced Research Projects Agency. Machine Reading Program
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|a United States. Air Force Research Laboratory (Prime contract no. FA8750-09-C-0181)
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|a United States. Defense Advanced Research Projects Agency. GALE Program (Contract No. HR0011-06-C-0022)
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|a Google (Firm)
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|a en_US
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|a Article
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|t Conference on Empirical Methods in Natural Language Processing 2010, Proceedings
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