CONTEXT-BASED SPELL CHECKER FOR SIDAAMU AFOO USING HYBRID APPROACH

Abstract

Spellcheck involves identifying and suggesting corrections for incorrectly spelled words within the text. Its integration spans various applications such as digitally correcting handwritten text, aiding user word corrections during retrieval, and more. This thesis outlines the creation, implementation, and assessment of a model intended to rectify both non-word and real-word errors. The central objective of this research is to devise a context-based spellchecker for Sidaamu afoo. This system relies on the language's error patterns, deduced from word sequences within input sentences. The chosen technique for this spellchecking entails an unsupervised statistical method, which is particularly beneficial for languages like Sidaamu afoo by enabling analysis without the need for extensive tagged datasets. The process of rectifying spelling unfolds through distinct phases: identifying errors, proposing potential corrections, and arranging these suggestions by priority. Error identification hinges on a combination of dictionary lookup and bigram analysis. Data for the dictionary and Bigram model, essential for error detection and correction, were collected from diverse sources by the researcher. Addressing non-word errors involves computing the similarity between the misspelled word and tokens in the dictionary, measured using the Levenshtein distance, resulting in ranking and correction suggestions. In cases of real-word errors, bigram frequency aids in error detection, while bigram probability informs the correction process for misspelled words. The experimental phase encompassed the utilization of 52,093 tokens and 5,788 tokens for model learning and testing, respectively. The outcome revealed a spellchecker recall score of 92.4% and an accuracy rate of 92.5% for both non-word and real-word errors. These findings, aligned with the gated result accuracy of 92.5%, underscore the system's capability to rectify Sidaamu afoo misspellings. Future enhancements could explore advanced neural architectures to improve model quality further