@article{MAKHILLIJSC202015421487,
    title = {Development of an Adjectival Phrase-Based English to Yorùbá Machine Translator},
    journal = {International Journal of Soft Computing},
    volume = {15},
    number = {4},
    pages = {97-102},
    year = {2020},
    issn = {1816-9503},
    doi = {ijscomp.2020.97.102},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2020.97.102},
    author = {Bolaji,Precious,Adebimpe,Olatayo and},
    keywords = {orthography,Machine translator,questionnaires,respondents,ADJP,JFLAP},
    abstract = {An Adjectival Phrase-based (ADJP) system
was developed in this article for English to Yorùbá
machine translation. The data for the developed system
was extracted from locally spoken words and stored in a
database. JFLAP was used to test the re-write rules and
grammar using parse trees and Python programming
language is the core programming language used in
developing the system. The developed translator was
evaluated by comparing expert’s translated phrases to that
of the developed translator and the experimental subject
respondents using the Mean Opinion Score (MOS)
technique based on word orthography. Results show that
the expert’s average accuracy was 100% while the
respondent’s was 76.3% and the developed machine
translator’s accuracy was 95.5%. In conclusion, the
developed system’s accuracy is close to the expert’s and
higher than that of the experimental subject respondent’s.}
    }