  The major contributions of this work include the empirical establishment of a
better performance for Yoruba embeddings from undiacritized (normalized)
dataset and provision of new analogy sets for evaluation. The Yoruba language,
being a tonal language, utilizes diacritics (tonal marks) in written form. We
show that this affects embedding performance by creating embeddings from
exactly the same Wikipedia dataset but with the second one normalized to be
undiacritized. We further compare average intrinsic performance with two other
work (using analogy test set & WordSim) and we obtain the best performance in
WordSim and corresponding Spearman correlation.
