Abstract
Global practice in computational linguistics demonstrates how machines can automatically find, extract, and understand arguments in texts. In the field of natural language, automated argumentation analysis is becoming increasingly important. Existing research mostly covers languages with welldeveloped resource bases, such as English, German, and others. Kazakh is virtually unexplored in the context of automated argumentation analysis, as it has high morphological complexity and belongs to the Turkic family. The study of computer argumentation in Kazakh-language texts is relatively new, due to the lack of annotated corpora with argumentative structure. Therefore, argumentation analysis is an essential component for assessing the persuasiveness of texts across various genres, as well as for effective analysis, discussion, and improvement of recommender systems. This study aims to contribute by providing a comprehensive overview of advanced methodologies for automatic analysis, argumentative ontology structures, including their detection, classification, visualization, and the application of neural networks. This work is the first systematic review consolidating existing research on argumentation modeling for the Kazakh language.