O‘ZBEK TILI GRAMATIKASIDAGI SO‘ZLARNI TURKUMLARIGA AJRATISHDA NLP MODELLARIDAN FOYDALANILANISH
Keywords:
HMM, CRF, POS-tagging, morphology, NLP, deep learning.Abstract
Part-of-speech tagging (POS-tagging) in Uzbek language grammar is a fundamental task in Natural Language Processing (NLP). This process is essential for extracting syntactic and morphological structure and enabling accurate word analysis. This article analyzes modern NLP models applied to Uzbek, including rule-based, statistical, and deep learning approaches. It focuses on the adaptation of HMM (Hidden Markov Model), CRF (Conditional Random Fields), and transformer-based models to Uzbek linguistic features. Furthermore, morphological complexity, the role of lexical resources, and the free word order of Uzbek are discussed as influential factors. The paper aims to highlight technical challenges in POS-tagging for Uzbek and explore advanced NLP solutions to address them.
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