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Item Open Access Open Vocabulary Model for Kazakh Language using Deep Neural Networks(Suleyman Demirel University, 2021) Sultanova N.Zh.Assessment of the current state of the scientific and technological problem being solved. For the past 25 years there has been a demand for software solutions related to text processing, which has repeatedly experienced periods of growth, related to the emergence of personal computers, and with the rapid development of the Internet, and the rapid development of the Internet, and, In this natural language remains the most important way of communication, be the input of the search query on the miniature screen of the mobile phone, hints of the car navigator or business correspondence. Practically in all such applications such or otherwise the language model is used. So, for a convenient input of texts on a mobile phone, it is necessary to use the predicate input system, which practically corresponds to the direct application of the language model; language model - an indefinite part of the system of speech recognition, including volume and vocal search; Linguistic models are used in machine translation systems, the quality of which at the moment is still far from ideal, but still grows steadilyItem Open Access VOICE ASSISTANT FOR MOBILE SYSTEMS WITH NATURAL LANGUAGE PROCESSING(Suleyman Demirel University, 2016) Kuanyshbay D.; Orynbetov Z.; Kariboz D.In order to handle some tasks by voice recognition it is really important that application will understand what user said. Which means that application must take a sentence that was entered by user and analyze and handle some tasks or etc. This paper is about natural language processing and analyzing sentences. In this article, we have briefly gone through the problems of NLP and some of the common techniques used in natural language analysis. These techniques are, however, very simple and fundamental. Many more complex and efficient approaches have been developed. However,in spite of these new developments, current state of the art is still capable of only limited tasks within restricted domains. Even though, work in the subject had began more than forty years ago, it is still in the very early stage of its development and there is definitely more to meet the eyes.Item Open Access Defining Semantically Close Words of Kazakh Language with Distributed System Apache Spark(MDPI, 2023) Ayazbayev D.; Bogdanchikov A.; Orynbekova K.; Varlamis I.This work focuses on determining semantically close words and using semantic similarity in general in order to improve performance in information retrieval tasks. The semantic similarity of words is an important task with many applications from information retrieval to spell checking or even document clustering and classification. Although, in languages with rich linguistic resources, the methods and tools for this task are well established, some languages do not have such tools. The first step in our experiment is to represent the words in a collection in a vector form and then define the semantic similarity of the terms using a vector similarity method. In order to tame the complexity of the task, which relies on the number of word (and, consequently, of the vector) pairs that have to be combined in order to define the semantically closest word pairs, A distributed method that runs on Apache Spark is designed to reduce the calculation time by running comparison tasks in parallel. Three alternative implementations are proposed and tested using a list of target words and seeking the most semantically similar words from a lexicon for each one of them. In a second step, we employ pre-trained multilingual sentence transformers to capture the content semantics at a sentence level and a vector-based semantic index to accelerate the searches. The code is written in MapReduce, and the experiments and results show that the proposed methods can provide an interesting solution for finding similar words or texts in the Kazakh language.Item Open Access KAZAKH LANGUAGE-BASED QUESTION ANSWERING SYSTEM USING DEEP LEARNING APPROACH(СДУ хабаршысы - 2023, 2023) Bilakhanova A. ; Ydyrvs A.; Sultanova N.Abstract. Deep learning advances have resulted in considerable gains in a variety of natural language processing applications, including questionanswering (QA) systems. QA systems are intended to retrieve data from big datasets and respond to user queries using natural language. Deep learning-based techniques have yielded encouraging results in the development of QA systems capable of providing consistent answers to a wide range of inquiries. This research presents a deep learning-based Kazakh language-based QA system. A pre-processing module is also included in the proposed system to improve the quality of the input text and the accuracy of the final output. The results reveal that the system has a high level of accuracy. This study promotes to the advancement of question-answering technology and contributes to the development of natural language processing tools in the Kazakh language.Item Open Access ARTIFICIALLY INTELLIGENT CONVERSATIONAL CHATBOTS: MAIN TYPES, PROGRAMMING ISSUES, AND POSSIBLE SOLUTIONS(2021 International Young Scholars' Conference, 2021) Momonov G.Abstract In recent years, artificially intelligent (AI) conversational chatbots have become widely used and proved to be a practical and effective support tool in many areas. Advances in machine learning and neural networks have made such proliferation of AI chatbots possible, yet there are still many programming issues and challenges in developing chatbots for various specific domains of human activity. This paper reviews types of artificially intelligent conversational chatbots and programming issues associated with each type. The following main types of chatbots are discussed: 1) script-based chatbots, 2) database-based chatbots, and 3) natural language processing-based chatbots. Key programming challenges in creating chatbots and existing solutions are reviewed.Item Open Access FAST AND RELATIVELY ACCURATE SENTIMENT ANALYSIS FOR THE KAZAKH LANGUAGE(2022 International Young Scholars' Conference, 2022) N. ManteyevaAbstract This paper constructs a fast and accurate sentiment analysis model for the Kazakh language. The main method for text classification is based on TF-IDF-based tokens trained with Logistic Regression. The processing and modeling stages are fully implemented in the PySpark framework. The proposed method has shown an accuracy level of 82% on an evenly distributed test dataset. As a byproduct of the work, we have collected a list of words in the Kazakh language that could signal the negativity/positivity of the given review.