As Uzbek language is agglutinative, has many morphological features which words formed by combining root and affixes. Affixes play an important role in the morp As Uzbek language is agglutinative, has many morphological features which words formed by combining root and affixes. Affixes play an important role in the morp

Word games are one of the most essential factors of vocabulary learning and matching letters to form words for children aged 5–12. These games help children to improve letter and word recognition, memory-building, and vocabulary retention skills. Since Uzbek is a low-resource language, there has not been enough research into designing word games for the Uzbek language. In this paper, we develop two models for designing the cubic-letter game, also known as the matching-letter game, in the Uzbek language, consisting of a predefined number of cubes, with a letter on each side of each six-sided cube, and word cards to form words using a combination of the cubes. More precisely, we provide the opportunity to form as many words as possible from the dataset, while minimizing the number of cubes. The proposed methods were created using a combination of a character-level n-gram model and letter position frequency in words at the level of vowels and consonants. To perform the experiments, a novel dataset, consisting of 4.5 k 3–5 letter words, was created by filtering based on child age groups for the Uzbek language, and three more datasets were generated, based on the support of experts for the Russian, English, and Slovenian languages. Experimental evaluations showed that both models achieved good results in terms of average coverage. In particular, the Vowel Priority (VL) approach obtained reasonably high coverage with 95.9% in Uzbek, 96.8% in English, and 94.2% in the Slovenian language in the case of eight cubes, based on the five-fold cross-validation method. Both models covered around 85% of five letter words in Uzbek, English, and Slovenian datasets, while this coverage was even higher (99%) in three letter words in the case of eight cubes.

Text classification is an important task in Natural Language Processing (NLP), where the goal is to categorize text data into predefined classes. In this study, we analyse the dataset creation steps and evaluation techniques of multi-label news categorisation task as part of text classification. We first present a newly obtained dataset for Uzbek text classification, which was collected from 10 different news and press websites and covers 15 categories of news, press and law texts. We also present a comprehensive evaluation of different models, ranging from traditional bag-of-words models to deep learning architectures, on this newly created dataset. Our experiments show that the Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) based models outperform the rule-based models. The best performance is achieved by the BERTbek model, which is a transformer-based BERT model trained on the Uzbek corpus. Our findings provide a good baseline for further research in Uzbek text classification.

Word games are of great importance in the acquisition of vocabulary and letter recognition among children, usually between the ages of 3 and 13, boosting their memory, word retention, spelling, and cognition. Despite the importance of these games, little attention has been paid to the development of word games for low-resource or highly morphologically constructed languages. This study develops an Advanced Cubic-oriented Game (ACG) model by using a character-level N-gram technique and statistics, commonly known as the matching letter game, wherein a player forms words using a given number of cubes with letters on each of its sides. The main objective of this study is to find out the optimal number of letter cubes while maintaining the overall coverage. Comprehensive experiments on 12 datasets (from low-resource and high-resource languages) incorporating morphological features were conducted to form 3–5-letter words using 7–8 cubes and a special case of forming 6–7-letter words using 8–9 cubes. Experimental evaluations show that the ACG model achieved reasonably high results in terms of average total coverage, with 89.5% for 3–5-letter words using eight cubes and 79.7% for 6–7-letter words using nine cubes over 12 datasets. The ACG model obtained over 90% coverage for Uzbek, Turkish, English, Slovenian, Spanish, French, and Malaysian when constructing 3–5-letter words using eight cubes.

This article presents the application of stemming, one of the most important aspects of NLP-Natural Language Processing, of the Uzbek verbs. The methodology is proposed for doing the stemming of the Uzbek verb words with a affix stripping approach whereas not including any lexicon. Verb affixes are classified into three classes and designed the finite state machines (FSMs) for each class according morphological rules. A stemming application for Uzbek verb words has been developed based the FSMs. Ushbu maqolada tabiiy tillarni qayta ishlash jarayoni (NLP–Natural Language Processing) ning eng muhim yoʻnalishlaridan biri hisoblangan stemming masalasini oʻzbek tili fe’l soʻz turkumi uchun qoʻllash qaraladi. Oʻzbek tilidagi fe’l soʻz turkumiga oid soʻzlar uchun qo‘shimchalarni qirqib olishga asoslangan stemming algoritmini chekli avtomatlar asosida yaratish masalasi yoritib berilgan. Fe’l soʻz turkumining qoʻshimchalari morfologik qoidalar asosida sinflarga ajratilgan va har bir sinf uchun qoʻshimchalarni qirqishga moʻljallangan chekli avtomatlar qurilgan. Ishlab chiqilgan chekli avtomatlar asosida stemming algoritmi ishlab chiqilgan.