Boago Okgetheng, Gabofetswe Malema, Ariq Ahmer, Boemo Lenyibi and Ontiretse Ishmael, University of Botswana, Botswana

Automatic spelling correction for a language is critical since the current world is almost entirely dependent on digital devices that employ electronic keyboards. Correct spelling adds to textual document accessibility and readability. Many NLP applications, such as web search engines, text summarization, sentiment analysis, and so on, rely on automatic spelling correction. A few efforts on automatic spelling correction in Bantu languages have been completed; however, the numbers are insufficient. We proposed a spell checker for typed words based on the Modified minimum edit distance Algorithm (MEDA), and the Syllable Error Detection Algorithm (SEDA). In this study, we adjusted the minimal edit distance Algorithm by including a frequency score for letters and ordered operations. The SEDA identifies the component of the word and the position of the letter which has an error. For this research, the Setswana language was utilized for testing, and other languages related to Setswana will use this spell checker. Setswana is a Bantu language spoken mostly in Botswana, South Africa, and Namibia and its automatic spelling correction are still in its early stages. Setswana is Botswana’s national language and is mostly utilized in schools and government offices. The accuracy was measured in 2500 Setswana words for assessment. The SEDA discovered incorrect Setswana words with 99% accuracy. When evaluating MEDA, the edit distance algorithm was utilized as the baseline, and it generated an accuracy of 52%. In comparison, the edit distance algorithm with ordered operations provided 64% accuracy, and MEDA produced 92% accuracy. The model failed in the closely related terms.

Bantu Spell Checker, Edit Distance algorithm, morphologically rich, Syllable Error Detection Algorithm.