Context-based Automatic Correction for the Uzbek Language

Correcting spelling mistakes in texts written in Uzbek is one of the important problems to be solved in natural language processing (NLP) tasks. In this research, a rule method for automatic spelling correction based on context-based and machine learning algorithm for Uzbek texts is presented. The method combines a noisy channel model with hidden markov models to correct a given word. This study differs from other studies by also considering the contextual information of the word within the sentence. The proposed method aims to integrate with other word-based spelling correction models.

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