Automatic Detection and Correction of Spelling Errors in Agglutinative Languages (A Case Study of the Uzbek Language)

One of the key directions in the field of Natural Language Processing (NLP) is the automatic detection and correction of spelling errors in texts. This task becomes particularly challenging in agglutinative languages such as Uzbek, where words are formed through the addition of numerous affixes. This paper analyzes algorithms for detecting and correcting spelling errors in the Uzbek language, their operational principles, and modern machine learning approaches. The study examines dictionary-based methods, the Levenshtein distance algorithm, N-gram models, and context-aware approaches based on neural networks. The findings demonstrate that a hybrid algorithm, combining multiple techniques, provides the most effective solution for Uzbek spell-checking.