Abstract
The Kazakh language, as an agglutinative and morphologically rich language, presents significant challenges for the development of natural language processing (NLP) tools. Traditional rule-based analyzers provide full coverage but lack flexibility; statistical and neural models handle disambiguation more effectively, yet require large annotated corpora and substantial computational resources. This paper presents a hybrid morphological analyzer that integrates Finite-State Transducers (FST), Conditional Random Fields (CRF), and transformer-based architectures (KazRoBERTa, mBERT). For the experiments, a new corpus, KazMorphCorpus-2025, was created, consisting of 150,000 sentences from diverse domains annotated for morphological analysis. Experimental evaluation demonstrated that the KazRoBERTa model consistently outperforms mBERT in terms of accuracy, F1-score, and prediction speed. The hybrid architecture effectively combines the exhaustive coverage of FST with the contextual disambiguation of neural networks, reducing errors associated with homonymy, borrowings, and long affixal chains. The results confirm that the proposed system achieves a balance between accuracy, efficiency, and scalability. The study underscores the practical significance of hybrid approaches for tasks such as spell checking, information retrieval, and machine translation in the Kazakh language, as well as their potential transferability to other low-resource Turkic languages. Future work will include the expansion of the corpus, integration of KazBERT and mBERT models, and validation of the proposed approach in applied NLP systems.