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Item Open Access KAZAKH HANDWRITING RECOGNITION(СДУ хабаршысы - 2023, 2023) Bazarkulova A.; Mutalivev Y.; Chazhabayev A.; Telman D. ; Bazarkulova D.Abstract. Recognition of handwritten text is one aspect of object recognition and known as handwriting detection cause of a computer’s potential to recognize and comprehend readable handwriting from resources including paper files, touch smart devices, images, etc. Data is categorized into a number of classes or groups using pattern recognition. The paper presents a successful experiment in recognizing handwritten Kazakh text using Convolutional Recurrent Neural Network based architectures and the Kazakh Autonomous Handwritten Text Dataset. The proposed algorithm achieved an overall accuracy of 86.36% and showed promising results. However, the paper suggests that further research could be conducted to improve the model, such as correlating and enlarging the database or incorporating other models and libraries. Additionally, the paper emphasizes the importance of considering language specifics when building a text recognition model, as modern algorithms that work well in one language may not guarantee the same performance in another.Item Open Access DEVELOPING SPEECH RECOGNITION APPLICATION IN KAZAKH LANGUAGE(Suleyman Demirel University, 2015) Aitimov A.K.; Amirgaliyev Y.N.Speech Recognition (is also known as Automatic Speech Recognition (ASR), or computer speech recognition) is the process of converting a speech signal to a sequence of words, by means of an algorithm implemented as a computer program. This paper is aimed to create speech recognition application in Kazakh language. The paper includes what is ASR and its architecture. Also, commands and actions of application, and result of tests.Item Open Access Speech Recognition of Kazakh Language(Faculty of Engineering and Natural Science, 2020) Aitimov K.In this research, an overage implementation of speech recognition revised. Here you can find algorithms and logic of how to convert digital signal into reasonable text. In common, all the signals recorded in m4a file format. You also will learn how to convert sound waves into digital signals, so you can use it wherever you like. In our days IT, technologies are expanding in an exponential level. There are many robots, which can understand human speech. Why we can’t create one of our own? Kazakh language will also be presented in technology world. That is our goal!Item Open Access KAZAK DİLİ’NİNTARİHİ KAYNAĞI OLAN “MÜNYETÜ’LGUZAT” ESERİNDEN ÖRNEKLER(Suleyman Demirel University, 2016) Auelbekova Ş.A.Kıpçak Memlukçuluğunun eski dilinde yazılmış en eski kitaplardan biri "MüniyatülGüzat" denir. Bu kitap Türkçeye çevrildiğinden beri orijinal Türkçe kelimeler. Kitap öncelikle savaş sanatını öğretmeyi amaçlıyor, bu yüzden yazılmış basit ve sanatsal ifade kullanmadan. Ana kelime hazinesi terimlerden oluşur askeri teçhizatla ilgili olarak, o sırada kullanılan silahların isimleri. Bu kitabın değeri yatıyor gerçek şu ki, o zamanın konuşma tarzı hakkında tarihsel bilgi kaynağıdır. Kitabı Kazak dilinin başlıca kaynaklarından biri olarak görmenin her türlü nedeni vardır. Eser, Türk dillerinin tipolojik özelliklerinin incelenmesine temel teşkil edebilir, ses yapıları, gelişimlerinin evrimsel tarihi, anlamsal doğası ve dilde uygulama. Bu, doğanın doğası hakkında bilgi içeren bir sergidir. türk dillerinde var olan kök kelimeler. Temelin doğasının gizemini ortaya çıkarır kelime hazinesi, dünyanın tüm zenginliğinin temelini oluşturan eski kök kelimelerin özüdür. modern Kazak dilinde kullanılan kelime bilgisi. Bu, en önemli eserlerden biridir. halkın maddi ve manevi zenginliğinin bir aynası olarak kabul edilir, onun tek tanığı uzun tarihsel gelişim. Bu kitap değerlidir çünkü bir kitabın sırlarını çözmeye yardımcı olacaktır. kazak dilinin uzun tarihi yolu.Item Open Access SPELL CHECKING APPLICATION IN KAZAKH LANGUAGE(Suleyman Demirel University, 2015) Aitimov A.K.; Amirgaliyev Y.N.In computing, a spell checker (or spell check) is an application program that flags words in a document that may not be spelled correctly. Spell checkers may be stand-alone, capable of operating on a block of text, or as part of a larger application, such as a word processor, email client, electronic dictionary, or search engine. However, in many languages such kind of application is not developed yet, and Kazakh language is one of such languages. This paper is aimed to create spell checker application both wird and sentences. Also the paper includes description of main modules which were used in creating the application, and result of the application test.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 COMPARISON OF DIFFERENT CLASSIFICATION MODELS FOR SENTIMENT ANALYSIS(СДУ хабаршысы - 2023, 2023) Makhul M.Abstract. In this work, we explored sentiment analysis techniques of texts using the example of product comments in the Kazakh language. To do this, we used machine learning methods such as Naive Bayes, Random Forest, Logistic Regression and Support Vector Machine, as well as text processing tools: CountVectorizer and TfidfVectorizer. In the process of work, experiments were carried out with different configurations of models and parameters of vectorizers. To assess the quality of the models, we used accuracy, precision, recall and F1-score metrics. The research findings indicated that the application of machine learning techniques make it possible to achieve high accuracy in sentiment analysis of comments. The best results were obtained using the Support Vector Machine and TfidfVectorizer. This study can be used to further improve the systems for sentiment analysis of comments in the Kazakh language, which can be useful in monitoring public opinion in various areas, including business.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.