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Educational Corpus of the Uzbek Language and its Opportunities
2023 8th International Conference on Computer Science and Engineering (UBMK), 2023The educational corpus is a language corpus based on school textbooks and dictionaries and is a structural type of the Uzbek National Corpus. According to the “Concept of the National Corpus of the Uzbek language”, the educational corpus of the Uzbek ...
Manzura Abjalova +2 more
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Development of Sentiment Analysis Algorithms of Uzbek Patient Reviews
Educational Data MiningThe presented work describes the training of three machine learning models (Logistic Regression, Support Vector Machine, Naive Bayes) for sentiment analysis of Uzbek user reviews in the field of medicine.
Rano Sayfullaeva +2 more
semanticscholar +1 more source
Towards Effective Named Entity Recognition in Uzbek Medical Contexts
IEEE Region International Conference on Computational Technologies in Electrical and Electronics EngineeringIn this research work, the authors developed an algorithm for recognizing named entities adapted for medical texts in the Uzbek language. The Python's Spacy library was chosen as the main technology for implementing the algorithm, including its ...
Davlatyor B. Mengliev +5 more
semanticscholar +1 more source
Automated Recognition of Named Entities and Dialect Standardization in Uzbek Legal Texts
2024 IEEE 3rd International Conference on Problems of Informatics, Electronics and Radio Engineering (PIERE)This study presents the development of a tool for identifying named entities in Uzbek legal texts. It should be noted, that besides of detecting named entities, the authors developed an algorithm, which is able to standardize word forms by replacing the ...
Davlatyor B. Mengliev +5 more
semanticscholar +1 more source
Building a Comprehensive Uzbek Lexicon: Bridging Dialects for Text Standardization
Educational Data MiningAs part of the study, the authors developed a dictionary of the formal Uzbek language and its dialects, which can be used in the tasks of standardizing mixed texts in various dialects of the Uzbek language into a single - formal format.
Davlatyor B. Mengliev +5 more
semanticscholar +1 more source
MorphUz: Morphological Analyzer for the Uzbek Language
2022 7th International Conference on Computer Science and Engineering (UBMK), 2022The Uzbek language is an agglutinative language in that words are derived from stems (root) by concatenating affixes. This property makes a large number of combinations of morphemes, and greatly increases the word-vocabulary size.
Nilufar Abdurakhmonova +2 more
semanticscholar +1 more source
Anthropology & Archeology of Eurasia, 2004
The author is Research fellow at the SOM's, University of London; E-mail: ailkhamov@yahoo ...
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The author is Research fellow at the SOM's, University of London; E-mail: ailkhamov@yahoo ...
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Automated Detection of Allusions in Uzbek Language: A Computational Approach
2024 IEEE 3rd International Conference on Problems of Informatics, Electronics and Radio Engineering (PIERE)Allusions play a significant role in literary and cultural works, serving as a tool for conveying deep meanings embedded by authors. In the Uzbek language, as in many other languages, allusions are commonly used to create more complex narratives, which ...
Davlatyor B. Mengliev +5 more
semanticscholar +1 more source
A Computational Approach to Recognizing Poetry Genres in Uzbek Texts
IEEE Region International Conference on Computational Technologies in Electrical and Electronics EngineeringIn the research paper, the authors propose an algorithm for detecting poetic elements, words, and phrases in Uzbek texts. In addition, during analyzing the algorithm classifies poetic tokens in of the of 2 poetic genres (comedy, drama).
Davlatyor B. Mengliev +5 more
semanticscholar +1 more source
USC: An Open-Source Uzbek Speech Corpus and Initial Speech Recognition Experiments
International Conference on Speech and Computer, 2021We present a freely available speech corpus for the Uzbek language and report preliminary automatic speech recognition (ASR) results using both the deep neural network hidden Markov model (DNN-HMM) and end-to-end (E2E) architectures.
M. Musaev +5 more
semanticscholar +1 more source

