20.500.12556/RUP-21966 TF-IDF-based classification of Uzbek educational texts Is open source the future of AI This paper presents a baseline study on automatic Uzbek text classification. Uzbek is a morphologically rich and low-resource language, which makes reliable preprocessing and evaluation challenging. The approach integrates Term Frequency–Inverse Document Frequency (TF–IDF) representation with three conventional methods: linear regression (LR), k-Nearest Neighbors (k-NN), and cosine similarity (CS, implemented as a 1-NN retrieval model). The objective is to categorize school learning materials by grade level (grades 5–11) to support improved alignment between curricular texts and students’ intellectual development. A balanced dataset of Uzbek school textbooks across different subjects was constructed, preprocessed with standard NLP tools, and converted into TF–IDF vectors. Experimental results on the internal test set of 70 files show that LR achieved 92.9% accuracy (precision = 0.94, recall = 0.93, F1 = 0.93), while CS performed comparably with 91.4% accuracy (precision = 0.92, recall = 0.91, F1 = 0.92). In contrast, k-NN obtained only 28.6% accuracy, confirming its weakness in high-dimensional sparse feature spaces. External evaluation on seven Uzbek literary works further demonstrated that LR and CS yielded consistent and interpretable grade-level mappings, whereas k-NN results were unstable. Overall, the findings establish reliable baselines for Uzbek educational text classification and highlight the potential of extending beyond lexical overlap toward semantically richer models in future work. Uzbek language text classification low-resource languages TF-IDF cosine similarity linear regression k-Nearest Neighbors uzbeški jezik klasifikacija besedil jeziki z omejenimi viri TF-IDF kosinusna podobnost linearna regresija k-najbližji sosedje true false true Angleški jezik Slovenski jezik Članek v reviji 2025-10-17 15:22:44 2025-10-17 15:22:45 2025-11-11 03:02:48 0000-00-00 00:00:00 2025 0 0 str. 1-13 iss. 19, [article no.] 10808 Vol. 15 Oct.-1 2025 0000-00-00 Zaloznikova NiDoloceno apc 0000-00-00 0000-00-00 2025-10-08 004:811.5:81'322 2076-3417 10.3390/app151910808 253709315 RAZ_Madatov_Khabibulla_2025.pdf RAZ_Madatov_Khabibulla_2025.pdf 1 83753809D57B917BE2F9B9F317E52201 852adac060e6311409eecc9ef9da7f9a544be30dbfe5ea31c5b72cfb5e8761d3 a4175966-ab5c-11f0-8f0b-005056ac49c0 https://repozitorij.upr.si/Dokument.php?lang=slv&id=31894 https://www.mdpi.com/2076-3417/15/19/10808 1 8a48e137-ab5c-11f0-8f0b-005056ac49c0 https://repozitorij.upr.si/Dokument.php?lang=slv&id=31893 Fakulteta za matematiko, naravoslovje in informacijske tehnologije 0 0 0