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Educational Corpus of the Uzbek Language and its Opportunities

2023 8th International Conference on Computer Science and Engineering (UBMK), 2023
The 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
semanticscholar   +1 more source

Development of Sentiment Analysis Algorithms of Uzbek Patient Reviews

Educational Data Mining
The 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 Engineering
In 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 Mining
As 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), 2022
The 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

Archeology of Uzbek Identity

Anthropology & Archeology of Eurasia, 2004
The author is Research fellow at the SOM's, University of London; E-mail: ailkhamov@yahoo ...
openaire   +1 more source

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 Engineering
In 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, 2021
We 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

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