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Enhanced Machine Learning Algorithm for Translation of English to Igbo Language

The research has designed a system that has done a morphological analysis of noun phrase and compound verb. Also, the system designed will translate a whole sentence indicating which words are noun and verb in it. Clustering was an unsupervised technique which was used to translate from English to Igbo language. In order to obtain our desired motives, object oriented analysis and design methodology were used. The system has been developed to make Igbo populaces to communicate well with most spoken English country along the global and strengthen the Igbo’s pole position in terms of research excellence. Furthermore, it will remove barriers to international trade that will keep Igbo small and medium companies from obtaining their complete economic standard by making ways into markets in other continents beyond our own. These goals lead us to develop a machine learning algorithm for translation of English into Igbo language. Machine learning algorithm for translation of English to Igbo language is the missing puzzle that will bring businesses to the people’s doorsteps. Besides, people that refused to acquire Igbo language are denying themselves pleasure of direct and unfiltered communication with others and thereby imprisoned themselves with the thrown of language.

Machine Learning, Algorithm, Clustering, Igbo, English, Compound Verb, Noun Phrase, Translation

Orji Ifeoma Maryann, Sylvanus Okwudili Anigbogu, Ekwealor Oluchukwu Uzoamaka, Chidi Ukamaka Betrand. (2022). Enhanced Machine Learning Algorithm for Translation of English to Igbo Language. Machine Learning Research, 7(1), 8-14.

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1. Abiola O. B, Adetunmbi A. O, Oguntimilehin A (2015) “Review of the Various Approaches to Text to Text Machine Translations” International Journal of Computer Applications. Vol 120 No 18, pp 7-12. ISSN: 0975-8887.
2. Agbeyangi, A. O., Eludiora, S. I., and Adenekan, D. I. (2015). “English to Yorùbá Machine Translation System using Rule-Based Approach”. Journal of Multidisciplinary Engineering Science and Technology (JMEST), Vol. 2 Issue 8, August, Nigeria.
3. Alon. L, Christian. M. and Roberto. A. (2020) “Building NLP Systems for Two Resource-Scarce Indigenous Languages: Mapudungun and Quechua”. Proceedings of the MT2020 workshop at MT Summit.
4. Chinenyeze C. E, Bennett E. O. and Taylor O. E. (2019) A Natural Language Processing System for English to Igbo Language Translation in Android. International Journal of Computer Science and Mathematical Theory ISSN 2545-5699 Vol. 5 No. 1.
5. Gammerschlaag, T. (2000) Deriving Argument Structure in Japanese V-V Compounds. Working Paper of the SFB Theorie Des Lexikons No. 282, University of Düsseldorf.
6. Goyal, V., and Lehal, G. S. (2010). “Hindi to Punjabi Machine Translation System”. Department of Computer Science, Punjabi University, Patiala, India.
7. Hana, B. D. (2016). “Amharic to English Language Translator for iOS”. Department of Information Technology, Helsinki Metropolia University, Finland.
8. Iheanetu. O., Michael. K. and Ojo. S. O (2019) “Hidden Markov-based Part-of-Speech Tagger for Igbo Language”.
9. Ifeanyi. R. N, Ugwu. C. and Adegbola. T. (2017) “Analysis and Representation of Igbo Text Document for a Text-Based System”. International Journal of Data Mining Techniques and Applications Volume: 06, Issue: 01, Page No. 26-32 ISSN: 2278-2419.
10. Odejobi. O. A., Ajayi. A. O, Lukman. A. and Safiriyu. I. E (2015) “A Web Based System for Supporting Teaching and Learning of Nigerian Indigenous Languaage”. Faculty of Technology Conference at Obafemi Awolowo University, Ile-Ife, Nigerian.
11. Oha, A. B. (2010) Verb Compounding in Igbo: A Morpho-Syntactic Analysis. Unpublished PhD Thesis, University of Nigeria, Nsukka.
12. Olufemi. D. N., Abimbola. R. I., Isacc. O. E and Olamide. E. O. (2017) “Computational Analysis of Igbo Numeral in a Number-To-Text Conversion System”. Journal of Computer and Education Research Decemeber. Volume 5. Issue 10.
13. Oxford Modern English Grammar (2011) Aart edition.
14. Sangeetha. J, S. and Jothilakshmi, R. N. (2014), “An Efficient Machine Translation System for English to Indian Languages Using Hybrid Mechanism” International Journal of Engineering and Technology (IJET), pp 1909-1919, Vol 6 No 4, ISSN: 0975-4024.
15. Štekauer, P., Valera, S. and Körtvélyessy, L. (2012). Word-Formation in the World’s Languages: A Typological Survey. Cambridge: Cambridge University Press.
16. UCLA (2014). Language materials project: Igbo.