Work place: Obafemi Awolowo University, Department of Computer Science & Engineering, Ile-Ife, 220005, Nigeria
E-mail: oodejobi@yahoo.com
Website:
Research Interests: Natural Language Processing
Biography
Odetunji A. Odejobi B.Sc. (Computer Engineering) First Class Honours; OAU, Ile-Ife, Nigeria, PhD(Computer Science) Aston University, United Kingdom, COREN Registered Engineer (Nigeria), CPN Registered.
Dr. Odejobi's research is in the area of Computing and Intelligent Systems Engineering with focus on speech and language engineering. Dr. Odejobi was a Visiting Scholar to the School of Information Technology and Electrical Engineering at the University of Queensland, Brisbane, Australia as a fellow of the United Nations University International Institute of Software Technology (UNU-IIST). He was a Commonwealth Scholar (British) at the University of Aston in Birmingham, United Kingdom. Dr. Odejobi also had visiting scholar positions at the Phonetics Laboratory of the University of Oxford, Oxford, England and The Centre for Speech Technology Research (CSTR), of the University of Edinburgh, in Scotland, United Kingdom. Dr. Odejobi was a Marie Curie Research Fellow on the Constraint Reasoning Extended to Enhance Decision (CREED) Project at the Cork Constraint Computation Centre (4C), of the University College Cork (UCC), Cork, Ireland (The Republic). He was a Research Academic in the same institution between 2008 and 2009. Dr. Odejobi is a consultant to a number of National and International organisations. For example, he served as a consultant to the African Languages Technologies Initiatives (Alt-I) on the Microsoft Vista Operating System Localisation Project. He is currently a Senior Lecturer In the Department of Computer Science and Engineering. Obafemi Awolowo University, Ile-Ife, Nigeria. He has many publications in Local and International Journals.
By Safiriyu I. Eludiora Odetunji A. Odejobi
DOI: https://doi.org/10.5815/ijmecs.2016.11.02, Pub. Date: 8 Nov. 2016
The study formulated a computational model for English to Yorùbá text translation process. The modelled translation process was designed, implemented and evaluated. This was with a view to addressing the challenge of English to Yorùbá text machine translator. This machine translator can translate modify and non-modify simple sentences (subject verb object (SVO)).
Digital resources in English and its equivalence in Yorùbá were collected using the home domain terminologies and lexical corpus construction techniques. The English to Yorùbá translation process was modelled using phrase structure grammar and re-write rules. The re-write rules were designed and tested using Natural Language Tool Kits (NLTKs). Parse tree and Automata theory based techniques were used to analyse the formulated model. Unified Modeling Language (UML) was used for the software design. The Python programming language and PyQt4 tools were used to implement the model. The developed machine translator was tested with simple sentences. The results for the Basic Subject-Verb-Object (BSVO) and Modified SVO (MSVO) sentences translation show that the total Experimental Subject Respondents (ESRs), machine translator and human expert average scores for word syllable, word orthography, and sentence syntax accuracies were 66.7 percent, 82.3 percent, and 100 percent, respectively. The system translation accuracies were close to a human expert.
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