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A Statistical Word-Level Translation Model for Comparable Corpora

dc.contributor.authorDiab, Monaen_US
dc.contributor.authorFinch, Steveen_US
dc.description.abstractIn this paper, we present a model of statistical word-level mapping for comparable corpora. The approach is based on the assumption that if two terms have close distributional profiles, their corresponding translations' distributional profiles should be close in a comparable corpus. The proposed model is described. A preliminary investigation on intralanguage comparable corpora is laid out. The preliminary results are >92% accurate, suggesting the feasibility of the model. The model needs to undergo some improvements and should be tested cross linguistically before assessing its significance. (Also cross-referenced as UMIACS-TR-2000-41, LAMP-TR-048)en_US
dc.format.extent686807 bytes
dc.relation.ispartofseriesUM Computer Science Department; CS-TR-4150en_US
dc.relation.ispartofseriesUMIACS; UMIACS-TR-2000-41en_US
dc.titleA Statistical Word-Level Translation Model for Comparable Corporaen_US
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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