This research paper presented an extensive and in-depth comparative examination of word-formation mechanisms in the Azerbaijani language in relation to several major world languages, specifically English, Russian, German, Turkish, and Persian. By situating Azerbaijani within a broader cross-linguistic framework, the study aimed to elucidate both typological particularities and universal patterns in morphological processes. The methodology integrated cognitive morphological analysis, corpus-based investigation, computational and digital resource modelling, and cross-linguistic typological comparison to investigate Azerbaijani morphological structures. The research demonstrated that Azerbaijani preserved its core agglutinative structure while developing hybrid formations through loan-affix integration, showing increased frequency of mixed morphological chains in digital corpora and expanding productive affixation patterns in response to contact-driven lexical influx. Empirical analysis showed that Azerbaijani morphology was both flexible and resilient, capable of generating novel lexical items and accommodating semantic shifts in response to social, technological, and intercultural developments. These findings underscored the dual character of morphological evolution: it revealed itself as universal in its structural tendencies while being uniquely shaped by the cultural and linguistic context of Azerbaijani speakers. By situating Azerbaijani morphology within the comparative landscape of world languages, this study contributed to a deeper understanding of cross-linguistic creativity, typological variation, and the interplay between morphology and sociolinguistic dynamics, offering insights relevant to theoretical linguistics, language teaching, and applied lexicography. The practical value of this research lies in providing linguists, lexicographers, educators, and digital language-technology developers with empirically grounded models of Azerbaijani word-formation that can be directly applied to dictionary compilation, curriculum design, automated morphology processing, and the development of NLP tools such as morphological analysers and spell-checkers
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Ramila Farajova (Mon,) studied this question.
synapsesocial.com/papers/69a75dcec6e9836116a280cb — DOI: https://doi.org/10.31548/philolog/3.2025.48
Ramila Farajova
Mìžnarodnij fìlologìčnij časopis
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