Creating an accurate and contextual AI model in different languages
Keywords:
artificial intelligence, multilingual models, context analysis, precision, natural language processing, machine learning, semantic modeling, language detection, multilingual platforms, evaluation criteriaAbstract
This article explores the development of an artificial intelligence (AI) model based on precision and context across multiple languages. In today’s globalized world, AI systems capable of effectively operating in multilingual environments are becoming increasingly significant. The study proposes an approach based on machine learning algorithms, natural language processing (NLP) technologies, and multilingual databases to ensure semantic accuracy and context preservation across languages. The key components of the model — language detection, semantic analysis, context modeling, and evaluation criteria — are analyzed. The model is tested using examples in Uzbek, English, and Russian languages, with its effectiveness evaluated based on statistical data and empirical results. The research findings can be applied to the development of multilingual digital platforms, translation systems, and educational environments
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