Dual Decomposition for Parsing with Non-Projective Head Automata. Koo, T.; Rush, A. M.; Collins, M.; Jaakkola, T.; and Sontag, D. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1288-1298, 2010.
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.
@inproceedings{KooEtAl_emnlp10,
author = {Terry Koo and Alexander M. Rush and Michael Collins and Tommi Jaakkola and David Sontag},
title = {Dual Decomposition for Parsing with Non-Projective Head Automata},
booktitle = {Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
pages = {1288-1298},
year = {2010},
keywords = {Machine learning, Natural language processing, Approximate inference in graphical models},
url_Paper = {http://people.csail.mit.edu/dsontag/papers/KooEtAl_emnlp10.pdf},
abstract = {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.}
}