Learning translation templates for closely related languages

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dc.citation.epage762en_US
dc.citation.spage756en_US
dc.citation.volumeNumber2773en_US
dc.contributor.authorAltıntaş, Kemalen_US
dc.contributor.authorGüvenir, H. Altayen_US
dc.coverage.spatialOxford, UKen_US
dc.date.accessioned2016-02-08T11:55:24Z
dc.date.available2016-02-08T11:55:24Zen_US
dc.date.issued2003en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionDate of Conference: KES: International Conference on Knowledge-Based and Intelligent Information and Engineering Systems 7th INternational Conferenceen_US
dc.descriptionDate of Conference: September 2003en_US
dc.description.abstractMany researchers have worked on example-based machine translation and different techniques have been investigated in the area. In literature, a method of using translation templates learned from bilingual example pairs was proposed. The paper investigates the possibility of applying the same idea for close languages where word order is preserved. In addition to applying the original algorithm for example pairs, we believe that the similarities between the translated sentences may always be learned as atomic translations. Since the word order is almost always preserved, there is no need to have any previous knowledge to identify the corresponding differences. The paper concludes that applying this method for close languages may improve the performance of the system.en_US
dc.identifier.doi10.1007/978-3-540-45224-9_102en_US
dc.identifier.doi10.1007/978-3-540-45224-9en_US
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11693/27511en_US
dc.language.isoEnglishen_US
dc.publisherSpringer, Berlin, Heidelbergen_US
dc.relation.isversionofhttps://doi.org/10.1007/978-3-540-45224-9_102en_US
dc.relation.isversionofhttps://doi.org/10.1007/978-3-540-45224-9en_US
dc.source.titleKnowledge-Based Intelligent Information and Engineering Systemsen_US
dc.subjectExample-Based Machine Translation (EBMT)en_US
dc.subjectLanguage Processingen_US
dc.subjectMachine Translationen_US
dc.subjectOperational Systemsen_US
dc.subjectExample Based Machine Translationsen_US
dc.subjectOriginal Algorithmsen_US
dc.subjectTranslation Templatesen_US
dc.subjectWord Ordersen_US
dc.subjectAlgorithmsen_US
dc.subjectComputer Scienceen_US
dc.subjectLinguisticsen_US
dc.subjectNeural Networksen_US
dc.subjectSemanticsen_US
dc.subjectComputational Linguisticsen_US
dc.subjectKnowledge Based Systemsen_US
dc.subjectComputer Programming Languagesen_US
dc.subjectTranslation (languages)en_US
dc.titleLearning translation templates for closely related languagesen_US
dc.typeConference Paperen_US

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