Integrating Multiple Knowledge Sources for Robust Semantic Parsing

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dc.creatorAtserias, Jordi
dc.creatorPadro, Lluis
dc.creatorRigau, German
dc.date2001-09-17
dc.date.accessioned2026-07-07T03:17:31Z
dc.date.available2026-07-07T03:17:31Z
dc.descriptionThis work explores a new robust approach for Semantic Parsing of unrestricted texts. Our approach considers Semantic Parsing as a Consistent Labelling Problem (CLP), allowing the integration of several knowledge types (syntactic and semantic) obtained from different sources (linguistic and statistic). The current implementation obtains 95% accuracy in model identification and 72% in case-role filling.
dc.identifierhttps://arxiv.org/abs/cs/0109023
dc.identifierhttp://arxiv.org/abs/cs/0109023
dc.identifierProceedings of Euroconference on Recent Advances in Natural Language Processing (RANLP'01), p.8-14. Tzigov Chark, Bulgaria. Sept. 2001
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/30746
dc.subjectComputation and Language
dc.subjectArtificial Intelligence
dc.subjectI.2.7
dc.titleIntegrating Multiple Knowledge Sources for Robust Semantic Parsing
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