Statistical A$^$ Dependency Parsing

Peter Dienes, Alexander Koller, and Marco Kuhlmann

In Prospects and Advances in the Syntax/Semantics Interface, Nancy, 2003.

Extensible Dependency Grammar (XDG; Duchier and Debusmann (2001)) is a recently developed dependency grammar formalism that allows the characterization of linguistic structures along multiple dimensions of description. It can be implemented efficiently using constraint programming (CP; Koller and Niehren 2002). In the CP context, parsing is cast as a search problem: The states of the search are partial parse trees, successful end states are complete and valid parses. In this paper, we propose a probability model for XDG dependency trees and an A-Star search control regime for the XDG parsing algorithm that guarantees the best parse to be found first. Extending XDG with a statistical component has the benefit of bringing the formalism further into the grammatical mainstream; it also enables XDG to efficiently deal with large, corpus-induced grammars that come with a high degree of ambiguity.

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BibTeX Entry
@INPROCEEDINGS{sxdg-nancy,
	title = {Statistical {A}$^*$ Dependency Parsing},
	year = {2003},
	author = {Peter Dienes and Alexander Koller and Marco Kuhlmann},
	pages = {85--89},
	booktitle = {Prospects and Advances in the Syntax/Semantics Interface},
	label = {dienes2003statistical},
	address = {Nancy},
	series = {Lorraine-Saarland Workshop Series},
	editor = {Denys Duchier}
}

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