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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
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| Volume 13 - Issue 5 |
| Published: October 2026 |
| Authors: O.A. Odeniyi, O.O. Ogunsuyi, A.R. Akinwumi |
10.5120/ijais6c1e39db34cb
|
O.A. Odeniyi, O.O. Ogunsuyi, A.R. Akinwumi . YorùbáÒwe: A Rule based Bidirectional Web System for Yorùbá–English Idiom and Proverb Translation with Context Aware Disambiguation. International Journal of Applied Information Systems. 13, 5 (October 2026), 29-46. DOI=10.5120/ijais6c1e39db34cb
@article{ 10.5120/ijais6c1e39db34cb,
author = { O.A. Odeniyi,O.O. Ogunsuyi,A.R. Akinwumi },
title = { YorùbáÒwe: A Rule based Bidirectional Web System for Yorùbá–English Idiom and Proverb Translation with Context Aware Disambiguation },
journal = { International Journal of Applied Information Systems },
year = { 2026 },
volume = { 13 },
number = { 5 },
pages = { 29-46 },
doi = { 10.5120/ijais6c1e39db34cb },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2026
%A O.A. Odeniyi
%A O.O. Ogunsuyi
%A A.R. Akinwumi
%T YorùbáÒwe: A Rule based Bidirectional Web System for Yorùbá–English Idiom and Proverb Translation with Context Aware Disambiguation%T
%J International Journal of Applied Information Systems
%V 13
%N 5
%P 29-46
%R 10.5120/ijais6c1e39db34cb
%I Foundation of Computer Science (FCS), NY, USA
Idioms and proverbs (òwe) are central to Yorùbá discourse, yet computational Yorùbá NLP has concentrated on compositional tasks, leaving non-compositional expressions largely unaddressed. This study presents YorùbáÒwe, a publicly deployed web application for bidirectional Yorùbá↔English idiom and proverb translation. Its engine combines input normalization, sliding-window proverb detection, exact lexicon matching, near-match retrieval, rule-based context disambiguation, and word-level fallback. The engine's production source code was evaluated directly through a multi-layer evaluation. An initial evaluation against a 13-entry seed lexicon traced every Yorùbá-side miss (69.23% exact-match recall) to a hard-coded 12-token segmentation ceiling. The lexicon was then expanded to approximately 578 entries from a 568-pair Yorùbá–English proverb corpus, which was divided deterministically (80/20, random seed 42) into a 454-record Development/Lexicon Set and a 114-record Held-Out Test Set. All 13-unit tests passed, and the production build succeeded. On the Development/Lexicon Set the system retrieved the matching entry for 451/454 records (99.34% exact-match rate) and handled 806/818 controlled variants (98.53%); these figures describe lexicon-supported behavior, not general translation accuracy. On the Held-Out Test Set, evaluated with the held-out entries removed from the lexicon, reference-overlap scores were very low (BLEU 0.26, chrF 6.64, ROUGE-L F1 6.41; 0/114 exact textual matches), whereas a ChatGPT baseline on the same 114 records scored BLEU 38.42, chrF 58.77, and ROUGE-L F1 66.00 (4/114 exact textual matches). These metrics measure similarity to a single reference translation and do not establish translation quality. A human evaluation of 20 selected proverbs by three evaluators (60 judgments) gave mean ratings of 4.72/5 for meaning preservation, 4.72/5 for English naturalness, and 4.83/5 for cultural appropriateness, with 59/60 (98.33%) judged acceptable; an earlier 77-respondent evaluation of the 13 seed entries is also reported. YorùbáÒwe is therefore characterised as a lexicon-based, retrieval-oriented system whose coverage depends on its stored proverb knowledge.