Word Game Modeling Using Character-Level N-Gram and Statistics
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/6/1380 |
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| author | Jamolbek Mattiev Ulugbek Salaev Branko Kavsek |
| author_facet | Jamolbek Mattiev Ulugbek Salaev Branko Kavsek |
| author_sort | Jamolbek Mattiev |
| collection | DOAJ |
| description | 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 (<i>VL</i>) 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. |
| first_indexed | 2024-03-11T06:13:35Z |
| format | Article |
| id | doaj.art-a76d15fac2ea4b05aed9ef61dc8dfb54 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T06:13:35Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-a76d15fac2ea4b05aed9ef61dc8dfb542023-11-17T12:27:46ZengMDPI AGMathematics2227-73902023-03-01116138010.3390/math11061380Word Game Modeling Using Character-Level N-Gram and StatisticsJamolbek Mattiev0Ulugbek Salaev1Branko Kavsek2Information Technologies Department, Urgench State University, Khamid Alimdjan 14, Urgench 220100, UzbekistanInformation Technologies Department, Urgench State University, Khamid Alimdjan 14, Urgench 220100, UzbekistanDepartment of Information Sciences and Technologies, University of Primorska, Glagoljaška 8, 6000 Koper, SloveniaWord 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 (<i>VL</i>) 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.https://www.mdpi.com/2227-7390/11/6/1380word game modelingletter frequencycharacter-level N-grammodel coveragestatistics |
| spellingShingle | Jamolbek Mattiev Ulugbek Salaev Branko Kavsek Word Game Modeling Using Character-Level N-Gram and Statistics Mathematics word game modeling letter frequency character-level N-gram model coverage statistics |
| title | Word Game Modeling Using Character-Level N-Gram and Statistics |
| title_full | Word Game Modeling Using Character-Level N-Gram and Statistics |
| title_fullStr | Word Game Modeling Using Character-Level N-Gram and Statistics |
| title_full_unstemmed | Word Game Modeling Using Character-Level N-Gram and Statistics |
| title_short | Word Game Modeling Using Character-Level N-Gram and Statistics |
| title_sort | word game modeling using character level n gram and statistics |
| topic | word game modeling letter frequency character-level N-gram model coverage statistics |
| url | https://www.mdpi.com/2227-7390/11/6/1380 |
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