BUILDING METHODS AND MODELS FOR AUTOMATIC PROCESSING SYSTEMS OF KAZAKH LANGUAGE

The increasing reliance on automated processing systems for language tasks necessitates the development of robust methods and models tailored to specific linguistic nuances. This paper focuses on the construction of methods and models for automatic processing systems dedicated to the Kazakh language. The unique characteristics and complexities of Kazakh pose challenges in natural language processing, requiring specialized approaches for tasks such as machine translation, sentiment analysis, and information retrieval. In this study, we propose novel methodologies and models to address these challenges, leveraging linguistic features intrinsic to Kazakh. Our contributions include the design of effective preprocessing techniques, the development of language models tuned to Kazakh syntax and semantics, and the evaluation of these models against benchmark datasets. Through rigorous experimentation, we demonstrate the efficacy of our proposed methods in enhancing the performance of automatic processing systems for the Kazakh language. This research contributes to the broader goal of advancing natural language processing capabilities for languages with distinct linguistic structures, fostering inclusive and effective communication technologies.