Detection of Translational Equivalence

dc.contributor.authorSmith, Noah A.en_US
dc.date.accessioned2004-05-31T23:11:05Z
dc.date.available2004-05-31T23:11:05Z
dc.date.created2001-05en_US
dc.date.issued2001-09-05en_US
dc.description.abstractI propose a general algorithm for detecting translational equivalence between text samples in different languages. This algorithm is based on current approaches to duplicate detection, and it relies on information which can be automatically learned from parallel text. I also show experimental results which support the hypothesis that translational equivalence is empirically observable. In addition, these results suggest profitable directions for improving performance on this recognition task. Cross-referenced as UMIACS-TR-2001-36 Cross-referenced as LAMP-TR-071en_US
dc.format.extent606271 bytes
dc.format.mimetypeapplication/postscript
dc.identifier.urihttp://hdl.handle.net/1903/1137
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-4253en_US
dc.relation.ispartofseriesUMIACS; UMIACS-TR-2001-36en_US
dc.relation.ispartofseriesLAMP-TR-071en_US
dc.titleDetection of Translational Equivalenceen_US
dc.typeTechnical Reporten_US

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