ARTIFICIAL INTELLIGENCE FOR MODELING LITERARY CHARACTERS OF TIMURID PRINCESSES IN UZBEK AND ENGLISH NOVELS

Аннотация

This study outlines a comprehensive framework combining Artificial Intelligence (AI) and Natural Language Processing (NLP) with comparative literary theory to examine and model the representation of Timurid princesses in Uzbek and English novels. Figures like Saray Mulk Khanum, Gawhar Shad Begum, and Khanzada Begum hold significant standing within Central Asian historiography and narrative prose. Yet, their portrayals undergo distinct stylistic shifts when translated into Western literary frameworks. By incorporating text mining, structural sentiment monitoring, semantic vector embedding, and character co-occurrence mapping, this paper demonstrates how digital methods complement traditional qualitative close readings. The algorithmic extraction reveals that Uzbek narratives prioritize the internal political agency, ethical jurisprudence, and institutional leadership of these matriarchs. In contrast, historical English frameworks frequently position them through exoticized tropes, domestic diplomatic bargaining chips, or distant aesthetic ideals. Ultimately, this integration establishes an automated, replicable methodology for cross-lingual character modeling, advancing structural clarity within Digital Humanities without replacing the nuanced interpretive synthesis of the human scholar.