This paper presents a dataset and approaches to named entity recognition (NLP) in Uzbek language, in a resource-constrained language environment. Despite the increase in NLP applications, the Uzbek language is still underrepresented, which underscores the importance of our work. Our dataset includes 1,160 sentences with nearly 19,000 word forms annotated for parts of speech and named entities, making it a valuable resource for linguistic research and machine learning applications in Uzbek. In addition, for practical application and experiments, the authors have developed two algorithms that, using this dictionary, identifies named entities in Uzbek language texts. In addition, the authors described the methodology for creating the dataset, the design of the algorithms, and their application to the Uzbek language. This study not only provides an important dataset for future named entity recognition(NER) tasks in the Uzbek language, but also offers a methodological basis for the use of vocabulary-based NER or Machine learning NER in other low-resource languages (e.g. Karakalpak). The dataset (and algorithms) we have developed can be used to create applications such as improved chatbot systems, text mining applications and other analytical tools for the Uzbek language, contributing to the development of those areas in the region for which these solutions will be developed.

In this study, the authors presented a dataset for named entity recognition in the Uzbek language. The dataset consists of 2000 sentences and 25,865 words, and the sources were legal documents and hand-crafted sentences annotated using the BIOES scheme. The study is complemented by the fact that the authors demonstrated the applications of the created dataset by training a language model using the CNN + LSTM architecture, which achieves high accuracy in NER tasks, with an F1 score of 90.8 %, precision of 93.9 %, and recall of 88.0 % on the test set. The proposed dataset and trained model contribute to the development of natural language processing in the Uzbek language. In addition, the authors also conducted an analysis of existing works, as well as a comparative analysis, which will help to identify the distinctive features and novelty of the proposed work. Moreover, in conclusion, the authors propose possible scenarios for the development of the work, in the form of further scaling of the dataset, as well as the use of other neural network architectures.

In this study, the authors propose an algorithm for recognizing legal documents in Karakalpak texts. To develop such an algorithm, similar scientific works were studied, the relevance of the current work and the problems that need to be solved were identified. The proposed algorithm is developed based on traditional rules, which include the rules of morphology of the Karakalpak language, as well as a dictionary of tagged words used to identify legal words and phrases from texts. The dictionary used contains more than 12,000 tagged words, which include both word roots and other forms concatenated to grammatical affixes. The authors also tested the algorithm, where high accuracy in identifying the necessary words was achieved. In particular, three samples were formed, each of which contained words and phrases of legal terminology in a certain amount. In conclusion, the authors added information regarding the proposed improvements and further prospects for the development of the algorithm.

This article presents an original approach to recognizing named entities in Karakalpak language texts. The approach involves the author’s algorithm, which uses This article presents an original approach to recognizing named entities in Karakalpak language texts. The approach involves the author’s algorithm, which uses

Currently, there is an active development of the Uzbek sector of the Internet. In it, as in other national sectors, the most common form of presentation of textual information is semi-structured documents, work that presupposes the availability of reliable algorithms for text analysis, including its lexical characteristics. The article offers an intelligent web application developed for morphological analysis of words in the Uzbek language. The web application is based on the concept of generation and stem analysis of the Uzbek language word forms. A well-known Porter algorithm was chosen as the basis for stemming. The morphoanalyzer generates word forms of the Uzbek language based on the division of words into certain classes, taking into account the specifics and structure of this language. For example, nouns can be classified by meaning (related, nominal), by quantity (singular and plural), by case, and also, by the endings of belonging (possessive).

In this study, the authors presented an algorithm for automatic sentiment analysis in school essays written in the Uzbek language. The algorithm is implemented on the basis of a on a convolutional neural network architecture, designed to classify text using TensorFlow and Keras. Authors created a training dataset consisting of almost 5000 sentences and phrases, most commonly used in everyday communication. The text data underwent preprocessing, including punctuation removal and conversion to lowercase, before being transformed into numerical representations using an embedding layer that was trained simultaneously with the model. Besides, the authors tested the effectiveness of the model, where the evaluation was carried out using such metric as precision. As a result of testing, the precision reached 88 for sentiment analysis in 50 essays, which consist of 811 sentences overall. Moreover, the authors conducted a comparative analysis of existing works and proposed further options for the development of the algorithm.