Automatic Spell Checker and Correction for Under-represented Spoken Languages: Case Study on Wolof

TL;DR

This paper develops a spell checker for Wolof, an under-represented African language, achieving high accuracy despite limited data, and introduces new linguistic resources for the language.

Contribution

It presents a novel Wolof spell checker using a trie, dynamic programming, and Levenshtein distance, along with creating linguistic resources for the language.

Findings

Predictive accuracy of 98.31%

Suggestion accuracy of 93.33%

Provides foundational resources for Wolof NLP

Abstract

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.