UzMorphAnalyser: A Morphological Analysis Model for the Uzbek Language Using Inflectional Endings
This work addresses a domain-specific problem for Uzbek language processing, providing a tool for morphological analysis, but it is incremental as it applies existing methods to a new language context.
The paper tackled morphological analysis for the Uzbek language, an agglutinative language with data sparsity issues, by developing a model that uses inflectional endings for stemming, lemmatizing, and extracting morphological information, achieving over 91% word-level accuracy on a test set of 5.3K words.
As Uzbek language is agglutinative, has many morphological features which words formed by combining root and affixes. Affixes play an important role in the morphological analysis of words, by adding additional meanings and grammatical functions to words. Inflectional endings are utilized to express various morphological features within the language. This feature introduces numerous possibilities for word endings, thereby significantly expanding the word vocabulary and exacerbating issues related to data sparsity in statistical models. This paper present modeling of the morphological analysis of Uzbek words, including stemming, lemmatizing, and the extraction of morphological information while considering morpho-phonetic exceptions. Main steps of the model involve developing a complete set of word-ending with assigned morphological information, and additional datasets for morphological analysis. The proposed model was evaluated using a curated test set comprising 5.3K words. Through manual verification of stemming, lemmatizing, and morphological feature corrections carried out by linguistic specialists, it obtained a word-level accuracy of over 91%. The developed tool based on the proposed model is available as a web-based application and an open-source Python library.