AUTOMATIC IDENTIFICATION OF SENTENCE COMPONENTS IN THE UZBEK LANGUAGE USING THE HIDDEN MARKOV MODEL
DOI:
https://doi.org/10.37547/Keywords:
NLP, HMM, Viterbi algorithm, BIO chunking, syntactic analysis, Uzbek language, machine learning.Abstract
This article investigates the application of the Hidden Markov Model (HMM) and the Viterbi algorithm in the automatic syntactic analysis of the Uzbek language. Uzbek belongs to the agglutinative group of the Turkic language family and has a distinctive syntactic structure. In this study, sentence components are labeled using the BIO (Begin, Inside, Outside) tagging scheme within a syntactic analysis system, and the statistical probabilities of the model are examined. The results demonstrate the effectiveness as well as the limitations of HMM in the syntactic analysis of the Uzbek language.
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