Dual Decomposition for Parsing with Non-Projective Head Automata. Koo, T., Rush, A. M., Collins, M., Jaakkola, T., & Sontag, D. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1288-1298, 2010.
Dual Decomposition for Parsing with Non-Projective Head Automata [pdf]Paper  abstract   bibtex   
This paper introduces algorithms for non-projective parsing based on dual decomposition. We focus on parsing algorithms for non-projective head automata, a generalization of head-automata models to non-projective structures. The dual decomposition algorithms are simple and efficient, relying on standard dynamic programming and minimum spanning tree algorithms. They provably solve an LP relaxation of the non-projective parsing problem. Empirically the LP relaxation is very often tight: for many languages, exact solutions are achieved on over 98% of test sentences. The accuracy of our models is higher than previous work on a broad range of datasets.

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