Abstract
One of the relevant issues of a natural language processing is word sense disambiguation. Homonyms are considered as an important element of determining the meaning of a word. Methods based on machine learning play a special role in solving this problem. Naive Bayes classifier is one of the important machine learning methods. When eliminating homonymy between different and grammatically similar groups of words in the Uzbek language, the Naive Bayes classifier differs from other methods in its simplicity and speed. This classifier is one of the most popular multi-class classification algorithms, and depending on the data in question, any of the 3 types of Naive Bayes algorithms (Gaussian, Polynomial, Bernoulli) can be used. This article scrutinizes the processes of using the classifier to identify homonymy between grammatically similar groups of words in the Uzbek language.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
