Exploiting Diversity in Natural Language Processing: Combining Parsers

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dc.creatorHenderson, John C.
dc.creatorBrill, Eric
dc.date2000-06-01
dc.date.accessioned2026-07-07T03:16:15Z
dc.date.available2026-07-07T03:16:15Z
dc.descriptionThree state-of-the-art statistical parsers are combined to produce more accurate parses, as well as new bounds on achievable Treebank parsing accuracy. Two general approaches are presented and two combination techniques are described for each approach. Both parametric and non-parametric models are explored. The resulting parsers surpass the best previously published performance results for the Penn Treebank.
dc.description8 pages
dc.identifierhttps://arxiv.org/abs/cs/0006003
dc.identifierhttp://arxiv.org/abs/cs/0006003
dc.identifierProceedings of the Fourth Conference on Empirical Methods in Natural Language Processing (EMNLP-99), pages 187-194. College Park, Maryland, USA. June, 1999
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/30281
dc.subjectComputation and Language
dc.subjectI.2.7
dc.titleExploiting Diversity in Natural Language Processing: Combining Parsers
dc.typetext

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