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.

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. Therefore, words are split into certain sub-word units and applied to text and speech processing applications. Proper sub-word units not only provide high coverage and smaller lexicon size, but also provide semantic and syntactic information that is necessary for downstream applications. This paper discusses a morphological analyzer tool for natural language processing and machine learning purpose. The tool named MorphUz, which can split a text of words into a sequence of morphemes. Morphological analyzer is one of the main part of the natural language processing. MorphUz analyzer is an open-source morphological analyzer for the Uzbek language. The MorphUz analyzer is available as a website for exploration. MorphUz analyzer implements the morphology of the Uzbek language following a two-level approach using stemming and suffix analyzer. The implementation of MorphUz analyzer done by using PHP and JavaScript scripts and MySQL database.

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).

Today we see the active development of natural language processing technologies, including the morphological analysis of word forms. In this context, the development of more universal (general) algorithms that we can work with several languages at once is of particular relevance. As a result, algorithms that can reliably process texts of one family will be much more popular than those that can work with only one language. In our work, we presented a flexible tool for working with Turkic languages, in particular, for the morphological analysis of words. As is known, the Turkic-speaking languages belong to agglutinative languages, which, in turn, are formed by the concatenation of morphemes. It follows from this that by means of various combinations of affixes it is possible to construct a huge number of words in each of the languages of this family. Our tool allowed to carry out morphological analysis for almost all Turkic languages.

This research paper presents a morphologically annotated dataset for the Uzbek language, specifically designed for morphological analysis algorithms. The dataset contains 3022 manually annotated word forms, each annotated with root, affix, and part-of-speech information. Two morphological analysis approaches were implemented and compared: a user-defined rule-based stemming algorithm and a conditional random fields (CRF)-based machine learning model. Additionally, comprehensive genre testing was conducted on legal, political-economic, and educational texts to assess generalizability. The dataset is publicly available in Excel format and is intended as a base resource for further research in the field of natural language processing in Uzbek, including applications in text generation, semantic analysis, and grammar correction.

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.