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Development of Bi-Directional English To Yoruba Translator for Real-Time Mobile Chatting
Agbonifo Oluwatoyin Catherine, Adewale Olumide Sunday, Olaniyan Julius
Pages - 18 - 33     |    Revised - 31-01-2020     |    Published - 29-02-2020
Volume - 11   Issue - 1    |    Publication Date - February 2020  Table of Contents
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KEYWORDS
Computational Linguistics, Corpus-based Machine Translation, Rule-based Machine Translation, English Language, Yoruba Language, Mobile Chatting.
ABSTRACT
Machine translation (MT) is a subfield of computational linguistics that investigates the use of computer software to translate text or speech from one natural language to another. Translating between English language and Yoruba language comes with some computational complexities such as syntactic and grammatical differences in the language pair. This paper aims at exploring a multi-layer hybridized language translation approach, which combines the Corpus-based and Rule-based approaches of machine translation to generate its outputs. A parallel corpus was built with texts from English and Yoruba languages and stored in My Structured Query Language (MySQL) database. One hundred and forty seven computational rules were manually formulated and also stored in MySQL database for generating sentences in both languages. A di-bilingual dictionary was developed, one of which stored words in English with their corresponding Yoruba counterparts and their equivalent parts of speech while the other dictionary stored words in Yoruba with their corresponding English counterparts and their equivalent parts of speech. A real time mobile chatting interface was developed for users’ interactions with themselves and the system. The research model was implemented using PHP for server-side scripting, JSON for data interchange and Java programming language for user interfaces accessible on users’ mobile phones. The Java programming language was coded in Android Studio 3.0 Integrated Development Environment. Two hundred and eleven sentences from Contemporary English Grammar were used for system testing and the result shows 95% accuracy compare with Google Translate.
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Dr. Agbonifo Oluwatoyin Catherine
Department of Computer Science, School of Computing, Federal University of Technology, Akure, Ondo-State - Nigeria
ocagbonifo@futa.edu.ng
Professor Adewale Olumide Sunday
Department of Computer Science, School of Computing, Federal University of Technology, Akure, Ondo-State - Nigeria
Mr. Olaniyan Julius
Department of Computer Science, School of Computing, Federal University of Technology, Akure, Ondo-State - Nigeria