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

arXiv: https://arxiv.org/abs/2305.12694 · HTML: https://mlsanity.com/paper/2305.12694/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 37 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 1 of 1)

- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)

## Built on by (most cited first, 2 of 2)

- [Grammatical Error Correction for Low-Resource Languages: The Case of Zarma](https://mlsanity.com/paper/2410.15539/) (2024)
- [Opportunities and Challenges of Natural Language Processing for Low-Resource Senegalese Languages in Social Science Research](https://mlsanity.com/paper/2601.09716/) (2025)

## Abstract (arXiv)

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.