Automatic Extraction of Semantic Classes from Syntactic Information in Online Resources

dc.contributor.authorDorr, Bonnie J.en_US
dc.contributor.authorJones, Dougen_US
dc.date.accessioned2004-05-31T22:32:56Z
dc.date.available2004-05-31T22:32:56Z
dc.date.created1995-12en_US
dc.date.issued1998-10-15en_US
dc.description.abstractThis paper addresses the issue of word-sense ambiguity in extraction from machine-readable resources for the construction of large-scale knowledge sources. We describe two experiments: one which took word-sense distinctions into account, resulting in 97.9% accuracy for semantic classification of verbs based on (Levin, 1993); and one which ignored word-sense distinctions, resulting in 6.3% accuracy. These experiments were dual purpose: (1) to validate the central thesis of the work of (Levin, 1993), i.e., that verb semantics and syntactic behavior are predictably related; (2) to demonstrate that a 20-fold improvement can be achieved in deriving semantic information from syntactic cues if we first divide the syntactic cues into distinct groupings that correlate with different word senses. Finally, we show that we can provide effective acquisition techniques for novel word senses using a combination of online sources. (Also cross-referenced as UMIACS-TR-95-65)en_US
dc.format.extent131721 bytes
dc.format.mimetypeapplication/postscript
dc.identifier.urihttp://hdl.handle.net/1903/734
dc.language.isoen_US
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_US
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md.)en_US
dc.relation.isAvailableAtTech Reports in Computer Science and Engineeringen_US
dc.relation.isAvailableAtUMIACS Technical Reportsen_US
dc.relation.ispartofseriesUM Computer Science Department; CS-TR-3481en_US
dc.relation.ispartofseriesUMIACS; UMIACS-TR-95-65en_US
dc.titleAutomatic Extraction of Semantic Classes from Syntactic Information in Online Resourcesen_US
dc.typeTechnical Reporten_US

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