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Scalable Discriminative Parsing for German

  • Generative lexicalized parsing models, which are the mainstay for probabilistic parsing of English, do not perform as well when applied to languages with different language-specific properties such as free(r) word order or rich morphology. For German and other non-English languages, linguistically motivated complex treebank transformations have been shown to improve performance within the framework of PCFG parsing, while generative lexicalized models do not seem to be as easily adaptable to these languages. In this paper, we show a practical way to use grammatical functions as first-class citizens in a discriminative model that allows to extend annotated treebank grammars with rich feature sets without having to suffer from sparse data problems. We demonstrate the flexibility of the approach by integrating unsupervised PP attachment and POS-based word clusters into the parser.

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Metadaten
Author:Yannick Versley, Ines Rehbein
URN:urn:nbn:de:bsz:mh39-56080
URL:http://dl.acm.org/citation.cfm?id=1697236&picked=prox
Parent Title (English):Proceedings of the 11th International Conference on Parsing Technologies (IWPT). 7-9 October 2009 Paris, France
Publisher:Association for Computational Linguistics
Place of publication:Stroudsburg, PA
Document Type:Conference Proceeding
Language:English
Year of first Publication:2009
Date of Publication (online):2016/11/21
Publicationstate:Veröffentlichungsversion
GND Keyword:Automatische Sprachanalyse; Deutsch; Grammatik; Syntaktische Analyse
First Page:134
Last Page:137
Dewey Decimal Classification:400 Sprache / 400 Sprache, Linguistik
Linguistics-Classification:Computerlinguistik
Open Access?:Ja
Licence (German):Es gilt das UrhG