Automatic content classification of texts in the karakalpak language: addressing the low-resource challenge through cross-lingual transfer learning and data augmentation techniques

Automatic text classification represents a fundamental task in natural language processing, yet its application to low-resource languages such as Karakalpak remains substantially underdeveloped. This comprehensive study addresses the critical gap in multilingual natural language processing by investigating methodologies for effective content classification of Karakalpak texts despite severe data scarcity constraints. We present an integrated approach combining cross-lingual transfer learning with advanced data augmentation techniques specifically tailored to morphologically rich, low-resource language contexts. Our methodology leverages pre-trained multilingual BERT models, applies targeted fine-tuning strategies, and implements synthetic data generation through machine translation and back-translation approaches. Empirical evaluation on Karakalpak news classification and sentiment analysis datasets demonstrates significant improvements over monolingual baseline approaches, achieving F1-scores of 0.87 on news classification and 0.79 on sentiment analysis tasks. We demonstrate that strategic combination of transfer learning and data augmentation mitigates resource scarcity limitations more effectively than either technique in isolation. Analysis reveals that morphological characteristics of Karakalpak, common to Turkic language families, enable effective knowledge transfer from related languages. Our findings establish that low-resource status need not prevent development of practical text classification systems when theoretically informed methodologies address linguistic and computational constraints.