A comprehensive NLP framework for the kazakh language: design, methods, and applications

Abstract

Kazakh language presents considerable obstacles for natural language processing owing to its agglutinative morphology, complex inflectional structure, insufficient annotated corpora, and the lack of comprehensive computational tools. This study introduces a comprehensive NLP framework for the Kazakh language, integrating linguistic resources, rule-based processing, and transformer-based methodologies into a cohesive architecture. The suggested system is intended to facilitate several stages of text analysis, encompassing tokenization, morphological analysis, lemmatization, part-of-speech tagging, named entity recognition, syntactic processing, and subsequent semantic applications. Special emphasis is placed on the design principles of QazNLP, the creation of lexical and annotated resources, and the adaptation of contemporary deep learning methodologies to the structural characteristics of the Kazakh language. The practical significance of the presented system is evidenced by its applicability to information extraction, text analytics, search, and question answering. The research demonstrates that an integrated framework may effectively underpin the advancement of both scholarly inquiry and practical applications in Kazakh language technologies. In the context of sustainable development, the proposed framework contributes to inclusive digital intelligence by supporting computational tools for an under-resourced language. QazNLP can improve access to Kazakh-language information in education, public administration, legal services, e-government platforms, and social analytics, thereby supporting more equitable and sustainable digital transformation.

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