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Measuring Verb Similarity

dc.contributor.authorResnik, Philipen_US
dc.contributor.authorDiab, Monaen_US
dc.date.accessioned2004-05-31T23:04:49Z
dc.date.available2004-05-31T23:04:49Z
dc.date.created2000-06en_US
dc.date.issued2000-06-21en_US
dc.identifier.urihttp://hdl.handle.net/1903/1080
dc.description.abstractThe way we model semantic similarity is closely tied to our understanding of linguistic representations. We present several models of semantic similarity, based on differing representational assumptions, and investigate their properties via comparison with human ratings of verb similarity. The results offer insight into the bases for human similarity judgments and provide a testbed for further investigation of the interactions among syn tactic properties, semantic structure, and semantic con tent. (Also cross-referenced as UMIACS-TR-2000-40, LAMP-TR-047)en_US
dc.format.extent133919 bytes
dc.format.mimetypeapplication/postscript
dc.language.isoen_US
dc.relation.ispartofseriesUM Computer Science Department; CS-TR-4149en_US
dc.relation.ispartofseriesUMIACS; UMIACS-TR-2000-40en_US
dc.relation.ispartofseriesLAMP-TR-047en_US
dc.titleMeasuring Verb Similarity
dc.typeTechnical Reporten_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


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