Automatic Spell Checker and Correction for Under-represented Spoken
  Languages: Case Study on Wolof
Thierno Ibrahima Cissé Fatiha Sadat
This paper presents a spell checker and correction tool specifically designed
for Wolof, an under-represented spoken language in Africa. The proposed spell
checker leverages a combination of a trie data structure, dynamic programming,
and the weighted Levenshtein distance to generate suggestions for misspelled
words. We created novel linguistic resources for Wolof, such as a lexicon and a
corpus of misspelled words, using a semi-automatic approach that combines
manual and automatic annotation methods. Despite the limited data available for
the Wolof language, the spell checker's performance showed a predictive
accuracy of 98.31% and a suggestion accuracy of 93.33%. Our primary focus
remains the revitalization and preservation of Wolof as an Indigenous and
spoken language in Africa, providing our efforts to develop novel linguistic
resources. This work represents a valuable contribution to the growth of
computational tools and resources for the Wolof language and provides a strong
foundation for future studies in the automatic spell checking and correction
field.