Applied morphological processing of English
Natural Language Engineering
A compact architecture for dialogue management based on scripts and meta-outputs
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Automatic extraction of subcategorization from corpora
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Comlex Syntax: building a computational lexicon
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Improving the accuracy of subcategorizations acquired from corpora
ACLstudent '04 Proceedings of the ACL 2004 workshop on Student research
Automatic acquisition of grammatical types for nouns
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Bootstrapping deep lexical resources: resources for courses
DeepLA '05 Proceedings of the ACL-SIGLEX Workshop on Deep Lexical Acquisition
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
YIWCALA '10 Proceedings of the NAACL HLT 2010 Young Investigators Workshop on Computational Approaches to Languages of the Americas
French parsing enhanced with a word clustering method based on a syntactic lexicon
SPMRL '11 Proceedings of the Second Workshop on Statistical Parsing of Morphologically Rich Languages
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We describe the automatic acquisition of a lexicon of verb subcategorisations from a domain-specific corpus, and an evaluation of the impact this lexicon has on the performance of a “deep”, HPSG parser of English. We conducted two experiments to determine whether the empirically extracted verb stems would enhance the lexical coverage of the grammar and to see whether the automatically extracted verb subcategorisations would result in enhanced parser coverage. In our experiments, the empirically extracted verbs enhance lexical coverage by 8.5%. The automatically extracted verb subcategorisations enhance the parse success rate by 15% in theoretical terms and by 4.5% in practice. This is a promising approach for improving the robustness of deep parsing.