Morphotactic Models and Algorithms of the Uzbek Language
Abstract
This article presents a comprehensive study of morphotactic models and algorithms developed for the Uzbek language. Given the agglutinative nature of Uzbek, morphotactics—rules governing the internal structure and sequence of morphemes within a word—play a crucial role in accurate morphological analysis and generation. The paper outlines rule-based and data-driven approaches to modeling Uzbek morphotactics, emphasizing the integration of grammatical constraints and affix ordering principles. It further discusses the implementation of finite-state transducers (FSTs) and machine learning techniques for automatic morphological tagging and generation. The proposed models aim to enhance the performance of natural language processing (NLP) applications such as part-of-speech tagging, lemmatization, and syntactic parsing for Uzbek. Evaluation results demonstrate that the developed algorithms achieve high accuracy in capturing the complex morphological patterns of the language. This research contributes to the advancement of computational resources for low-resourced Turkic languages.