Procedure for quantitatively comparing the syntactic coverage of English grammars
HLT '91 Proceedings of the workshop on Speech and Natural Language
Parsing inside-out
Exploiting auxiliary distributions in stochastic unification-based grammars
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
A state-transition grammar for data-oriented parsing
EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
A DOP model for semantic interpretation
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
A probabilistic corpus-driven model for lexical-functional analysis
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Parsing with the shortest derivation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Estimators for stochastic "Unification-Based" grammars
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
The Penn Treebank: annotating predicate argument structure
HLT '94 Proceedings of the workshop on Human Language Technology
Parsing with the shortest derivation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
An improved parser for data-oriented lexical-functional analysis
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
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This paper presents an empirical assessment of the LFG-DOP model introduced by Bod & Kaplan (1998). The parser we describe uses fragments from LFG-annotated sentences to parse new sentences and Monte Carlo techniques to compute the most probable parse. While our main goal is to test Bod & Kaplan's model, we will also test a version of LFG-DOP which treats generalized fragments as previously unseen events. Experiments with the Verbmobil and Homecentre corpora show that our version of LFG-DOP outperforms Bod & Kaplan's model, and that LFG's functional information improves the parse accuracy of tree structures.