The main challenge of resource-poor languages—namely, the lack of sufficiently large and linguistically informed datasets for training neural models—is addressed in this paper by developing a dataset generation technology based on a Complete Set of Endings (CSE) morphological model for Turkic languages. Building on this technology, we propose a CSE-Guided Framework for morphology-aware statistical tokenization and neural model segmentation, with Kazakh as a case study. Applying the proposed CSE-guided approach to adapt well-known tokenizers for Kazakh leads to measurable reductions in neural model training time (up to approximately 33%) in our experimental setting, primarily due to shorter tokenized sentence lengths. In addition, we extend the SOTA FEMSeg-CRF architecture by incorporating Kazakh vowel–consonant harmony rules at the embedding generation stage. Within the proposed framework, training on a corpus of CSE-generated wordforms results in the FEMSegₖazᵥ2 model, which is evaluated using intrinsic segmentation metrics. Training on a CSE-segmented sentence corpus yields FEMSegₖazᵥ3, which is further assessed using intrinsic, extrinsic, and external evaluation on a manually prepared gold-standard dataset. The paper presents a CSE-guided framework for morphology-aware tokenization and segmentation for Turkic languages, supported by corpus construction, model extensions, and multi-level evaluation. The proposed CSE-Guided Framework can potentially be adapted for other Turkic languages.
Tukeyev et al. (2026) studied this question.