Statistical Parsing with Context-Free Filtering Grammar

摘要

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 lexi-calized 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 TueBa-D/Z treebank [1].