Dual decomposition for parsing with non-projective head automata

Terry Koo, Alexander M. Rush, Michael Collins, Tommi Jaakkola, David Sontag

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationEMNLP 2010 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
Pages1288-1298
Number of pages11
StatePublished - 2010
EventConference on Empirical Methods in Natural Language Processing, EMNLP 2010 - Cambridge, MA, United States
Duration: Oct 9 2010Oct 11 2010

Publication series

NameEMNLP 2010 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference

Other

OtherConference on Empirical Methods in Natural Language Processing, EMNLP 2010
CountryUnited States
CityCambridge, MA
Period10/9/1010/11/10

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

Fingerprint Dive into the research topics of 'Dual decomposition for parsing with non-projective head automata'. Together they form a unique fingerprint.

  • Cite this

    Koo, T., Rush, A. M., Collins, M., Jaakkola, T., & Sontag, D. (2010). Dual decomposition for parsing with non-projective head automata. In EMNLP 2010 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference (pp. 1288-1298). (EMNLP 2010 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference).