O‘ZBEK TILI MATNLARINI STEMLASH ALGORITMLARI
Keywords:
stemming, Uzbek language, Snowball algorithm, natural language processing, word normalization, text analysis.Abstract
This thesis discusses one of the key concepts in natural language processing — stemming, which is the process of reducing words to their root form. Stemming plays a crucial role in areas such as automatic text analysis, search engines, translation systems, and document classification. While there are effective stemming algorithms for English, Russian, and German, there is still a lack of efficient stemmers for the Uzbek language. Therefore, this paper theoretically explores the idea of creating an Uzbek stemmer based on the Snowball algorithm. The differences between stemming, tokenization, and lemmatization are highlighted, and various application areas in Uzbek — such as chatbots, machine translation, text analysis, social media monitoring, and digital dictionary creation — are discussed. The thesis also addresses the future prospects of developing high-performance stemmers for Uzbek and integrating them into artificial intelligence systems.
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