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The field of natural language processing enables machines to read and understand the languages human being
speaks. Developing a computational model for English language and Yoruba language noun-phrases involve a
profound understanding of the syntactic and grammatical features of the two languages as well as their
vocabularies since they are not related syntactically and grammatically. Twenty nine rules were formulated for
the noun phrase translations which were specified using the Context Free Grammar (CFG). We then modeled
and recognized the grammar of the language using the Finite State Automata (FSA) whose operations was based
on the first set techniques. The first sets techniques allow the parser to choose which production rule to apply
based on the first input word of an input phrase. We also developed a bilingual lexicon which is made up of
words in English language with their corresponding Yoruba counterparts and their equivalent part of speech.
The model was implemented using PhP programming language and MySQL and was tested on one hundred and
sixty randomly selected noun-phrases from daily news, and gives accuracy of 91% which is quite encouraging.
The system if fully developed will go a long way in preventing the extinction threat of the Yoruba language.
Keywords: Natural Language Processing, English, Yoruba, Noun-Phrases, Translation System, Context-Free
Grammar and Finite State Automata