Dual Decomposition for Parsing with Non-Projective Head Automata

This paper introduces algorithms for nonprojective parsing based on dual decomposition. We focus on parsing algorithms for nonprojective 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.

• Type: Journal
• Year: 2010
• Where: EMNLP
• Authors: Terry Koo, Alexander M. Rush, Michael Collins, Tommi Jaakkola, David Sontag
• Keywords: Algorithms, Dual Decomposition, EMNLP 2010, LP Relaxation, Natural Language Processing