English–Afaan Oromoo Machine Translation: an Experiment Using Statistical Approach

dc.contributor.advisorEisele, Andreas (PhD)
dc.contributor.authorAdugna, Sisay
dc.date.accessioned2021-04-01T08:50:47Z
dc.date.accessioned2023-11-18T12:47:20Z
dc.date.available2021-04-01T08:50:47Z
dc.date.available2023-11-18T12:47:20Z
dc.date.issued2009-04-18
dc.description.abstractMachine Translation (MT) refers to the use of a machine for performing translation task which converts text in one Natural Language into another Natural Language. It can have many applications like cross-linguistic information retrieval and speech to speech translation systems. It can also assist professional translators by producing draft quality output that reduces cost that would be incurred if translation and typing was done manually from scratch. English is the lingua franca of online information and Afaan Oromoo is one of the most resource scarce languages. For this reason, monolingual Afaan Oromoo speakers need to use documents written in other languages, among which English is the most popular one. To satisfy this need, translation of the English documents to Afaan Oromoo, and thus, making these online documents available in Afaan Oromoo is vital in addressing the language barrier thereby reducing the effect of digital divide. Therefore, this thesis is focused on the development of a prototype English-Afaan Oromoo machine translation system using statistical approach, i.e, without explicit formulation of linguistic rules, as this approach involves low cost and swiftest way available these days. Using limited corpus of about 20, 000 bilingual sentences, a translation accuracy of 17.74% was achieved.en_US
dc.identifier.urihttp://etd.aau.edu.et/handle/12345678/25893
dc.language.isoenen_US
dc.publisherAddis Ababa Universityen_US
dc.subjectEnglishen_US
dc.subjectAfaan Oromooen_US
dc.subjectMachine Translationen_US
dc.subjectExperiment Usingen_US
dc.subjectStatistical Approachen_US
dc.titleEnglish–Afaan Oromoo Machine Translation: an Experiment Using Statistical Approachen_US
dc.typeThesisen_US

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