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].
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Demko, M., Penn, G. (2009). Statistical Parsing with Context-Free Filtering Grammar. In: Gao, Y., Japkowicz, N. (eds) Advances in Artificial Intelligence. Canadian AI 2009. Lecture Notes in Computer Science(), vol 5549. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01818-3_8
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DOI: https://doi.org/10.1007/978-3-642-01818-3_8
Publisher Name: Springer, Berlin, Heidelberg
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