Economic aspects of error identification in semi-structured publications in the state language

Due to the rapid growth of information on the Internet and social networks, research in the field of computational linguistics has become very relevant. The volume of information that people and machines create in natural language needs to be processed, analyzed and verified. Information retrieval systems, dialog systems, and machine translation tools are used for this. The range of automatic text processing systems is very wide, it covers various tasks. Finding errors in texts and words, identifying and correcting incorrect words is one of the most important tasks of natural language processing (NLP). The article provides an overview of semi-structured data, methods and technologies for identifying incorrect words in natural languages. The paper gives an overview of semi-structured data, methods and techniques for detecting incorrect words in natural languages. The aim of the research is to develop an effective approach for detecting and correcting errors occurring in Kazakh-language texts, especially in the context of limited resources and unstructured data. The research includes the use of machine learning techniques as well as economic analysis of the costs of developing and implementing such solutions. The proposed approach facilitates the automation of text verification, which can significantly reduce the cost of manual data processing and improve the quality of information in various spheres, including business and public administration.