Framework for Many To One Machine Translation

Show simple item record Kituku, Benson Muchemi, Lawrence Nganga, Wanjiku 2021-07-02T07:17:59Z 2021-07-02T07:17:59Z 2016-05
dc.identifier.issn 2277-128X
dc.description.abstract The frequent domestic and international exchanges have created an opportunity for machine translation toflourish since human translators cannot cater for thetranslationdemand. However, there is slow pace in the development of machine translation tools using the one (source language) to one (target language) framework. The paper proposes a many (source languages) to one (target language) conceptual framework that will ensure faster and efficient development of machine translation tools using the Interlingua rule based machine translation approach. The many to one framework make use of shallow structure: lexical similarity and syntacticsimilarity and deep structure: a unique universal intermediate representation language. A formal computational grammar willbeneededand is exemplified by use of two under resourced languages: Swahili and Kikamba. Evaluation model is proposedat the end. en_US
dc.language.iso en en_US
dc.publisher International Journal of Advanced Research inComputer Science and Software Engineering en_US
dc.subject Lexical similarity en_US
dc.subject Interlingua en_US
dc.subject many to one and framework en_US
dc.subject under resourced language en_US
dc.title Framework for Many To One Machine Translation en_US
dc.type Article en_US

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