In the realm of computational linguistics, the development of robust morphological analyzers for under-resourced languages remains a critical challenge. The Uzbek language, with its rich agglutinative structure, presents unique morphological phenomena that have not been thoroughly addressed in existing linguistic technology. This research tackles the gap by presenting a comprehensive morphological analyzer tailored specifically for Uzbek, aiming to enhance natural language processing (NLP) applications and improve the accessibility of the language in digital environments.

The methodology employed in this study is grounded in a dual approach that combines rule-based and statistical techniques. The researchers constructed a morphological lexicon that encompasses over 50,000 entries, capturing the diverse forms of Uzbek words. They utilized a corpus of authentic Uzbek texts to train and evaluate the analyzer, ensuring that the model reflects real-world usage. The system integrates a finite-state transducer (FST) framework, which allows for efficient processing of morphological rules, including inflection, derivation, and compounding. This hybrid model not only increases accuracy but also provides a scalable solution for future expansions, accommodating the dynamic nature of the Uzbek language.

Key findings reveal that the morphological analyzer achieves an impressive accuracy rate of 92% in identifying and analyzing word forms. The system effectively handles complex morphological constructions, such as suffixation and vowel harmony, which are characteristic of Uzbek. Additionally, comparative evaluations against existing analyzers demonstrate that this new tool significantly outperforms previous models, particularly in its ability to parse compound words and handle irregular forms. The results indicate that the morphological analyzer can reliably support downstream NLP tasks, such as syntactic parsing and machine translation, thereby facilitating more nuanced language processing.

The broader implications of this research extend beyond Uzbek language processing, contributing to the field of NLP by providing a framework for developing similar tools for other under-resourced languages. As the demand for inclusive language technologies grows, this morphological analyzer serves as a model for addressing the linguistic complexities inherent in agglutinative languages. By enhancing the digital representation of Uzbek, this work not only supports linguistic research but also promotes cultural preservation and accessibility in the digital age. For further details on the methodology and findings, see the comprehensive study published in ScienceDirect.