| Title: | TF-IDF-based classification of Uzbek educational texts |
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| Authors: | ID Madatov, Khabibulla (Author)
ID Sattarova, Sapura (Author)
ID Vičič, Jernej (Author) |
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| Files: |  RAZ_Madatov_Khabibulla_2025.pdf (286,87 KB)
MD5: 83753809D57B917BE2F9B9F317E52201
 https://www.mdpi.com/2076-3417/15/19/10808
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FAMNIT - Faculty of Mathematics, Science and Information Technologies
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| Abstract: | 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. |
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| Keywords: | Uzbek language, text classification, low-resource languages, TF-IDF, cosine similarity, linear regression, k-Nearest Neighbors |
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| Publication version: | Version of Record |
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| Publication date: | 08.10.2025 |
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| Year of publishing: | 2025 |
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| Number of pages: | str. 1-13 |
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| Numbering: | Vol. 15, iss. 19, [article no.] 10808 |
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| PID: | 20.500.12556/RUP-21966  |
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| UDC: | 004:811.5:81'322 |
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| ISSN on article: | 2076-3417 |
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| DOI: | 10.3390/app151910808 |
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| COBISS.SI-ID: | 253709315 |
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| Publication date in RUP: | 17.10.2025 |
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| Views: | 311 |
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| Downloads: | 3 |
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