On dual decomposition and linear programming relaxations for natural language processing

• Main Authors: Rush, Alexander Matthew (Contributor), Sontag, David Alexander (Contributor), Collins, Michael (Contributor), Jaakkola, Tommi S. (Contributor)
• Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
• Format: Article
• Language: English
• Published: Association for Computational Linguistics, 2011-05-18T20:44:45Z.
• Subjects: Article
• Online Access: Get fulltext

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.
United States. Defense Advanced Research Projects Agency. Machine Reading Program
United States. Air Force Research Laboratory (Prime contract no. FA8750-09-C-0181)
United States. Defense Advanced Research Projects Agency. GALE Program (Contract No. HR0011-06-C-0022)
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