Statistical Parsing with Context-Free Filtering Grammar

Authors:
Michael Demko;Gerald Penn
Affiliations:
Department of Computer Science, University of Toronto, M5S 3G4;Department of Computer Science, University of Toronto, M5S 3G4
Venue:
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
2009

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Abstract

Statistical parsers that simultaneously generate both phrase-structure and lexical dependency trees have been limited to date in two important ways: detecting non-projective dependencies has not been integrated with other parsing decisions, and/or the constraints between phrase-structure and dependency structure have been overly strict. We introduce context-free filtering grammar as a generalization of a lexicalized factored parsing model, and develop a scoring model to resolve parsing ambiguities for this new grammar formalism. We demonstrate the new model's flexibility by implementing a statistical parser for German, a freer-word-order language exhibiting a mixture of projective and non-projective syntax, using the TüBa-D/Z treebank [1].