Integrating morphological stemming and syntactic parsing for low-resource Uzbek texts

Abstract

In the context of the lack of language resources for the Uzbek language, the development of complex tools for automatic text processing is particularly relevant. This article proposes a hybrid approach that combines preliminary morphological normalization of word forms with subsequent syntactic analysis of Uzbek sentences. At the first stage, stemming and lemmatization are performed using rules and dictionary resources, which allows obtaining canonical forms of words and reducing the degree of ambiguity. At the next stage, a trained model based on a syntactic parser (spaCy) determines grammatical relations between words. Experiments conducted on a corpus of multi-genre Uzbek texts demonstrated an improvement in the quality of syntactic analysis using morphological normalization: accuracy (Precision) reached 92%, recall (Recall) - about 91%, and F1-measure - about 91%. A comparative analysis with a model without preliminary normalization showed a decrease in quality by several percentage points, which emphasizes the rather important role of the morphological stage. The results obtained indicate the prospects of the proposed solution and create a basis for further development of tools for processing Uzbek texts.

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