THE CLASH OF AGGLUTINATIVE AND ANALYTIC LANGUAGES: CHALLENGES OF PROCESSING KARAKALPAK MORPHOLOGY IN ARTIFICIAL INTELLIGENCE SYSTEMS (A CASE STUDY OF ENGLISH)

Abstract

This study examines the challenges of processing Karakalpak morphology in artificial intelligence systems through a comparative analysis of Karakalpak and English languages. Karakalpak, as an agglutinative language, exhibits complex morphological structures characterized by extensive suffixation, rich inflectional patterns, and productive word formation processes. In contrast, English represents an analytic language in which grammatical relationships are expressed primarily through word order and auxiliary elements rather than morphological changes. These typological differences create significant difficulties for natural language processing applications, including morphological analysis, machine translation, speech recognition, information retrieval, and language modeling. The paper discusses the limitations of existing AI technologies when applied to low-resource agglutinative languages and highlights the need for specialized linguistic resources, annotated corpora, and morphology-aware computational models. The findings emphasize the importance of developing language-specific approaches to improve the accuracy and effectiveness of AI systems for Karakalpak and other agglutinative languages.