Аннотация
This paper presents a linguistically constrained neural approach to morphological segmentation for low-resource Turkic languages, with a case study on Turkmen. The proposed method combines large-scale training data generated by a Complete Set of Endings (CSE) model with a neural architecture augmented with explicit phonological inductive biases. Unlike prior FEMSeg-based architectures that rely on convolutional and Transformer layers for implicit feature learning, the proposed model, LCMSeg (Linguistically Constrained Morphological Segmentation), introduces vowel/consonant indicators and harmony-class embeddings, both of which are directly derived from linguistic rules. The constraints are implemented as inductive biases. The CSE framework serves as a data-generation mechanism, producing a segmented corpus of 270k sentences used for training. The neural model learns to approximate the segmentation function induced by the CSE annotations while generalizing beyond the limitations of rule-based methods. Experiments conducted on training sets of 10k to 80k sentences demonstrate consistent improvements, achieving up to 99.76% token accuracy and 99.53% morpheme accuracy. Evaluation on the FLORES-200 benchmark confirms strong generalization under domain shift, with harmony consistency reaching 98.9%. The results show that explicitly encoding phonological structure provides a strong inductive bias, particularly beneficial in low-resource settings. The proposed framework offers a scalable and linguistically grounded solution for morphological segmentation in Turkic languages.