This paper discusses the application of machine learning (ML) in text categorization, a task of assigning predefined categories to documents. The ML approach involves automatically learning the characteristics of categories from preclassified documents, offering advantages over knowledge engineering methods, such as higher effectiveness and reduced reliance on expert input. The paper reviews key aspects of text categorization, including document representation, classifier construction, and evaluation. It highlights the importance of text categorization in various applications, such as document indexing, filtering, and word sense disambiguation. The paper also discusses the differences between single-label and multi-label categorization, as well as the distinction between document-pivoted and category-pivoted approaches. It emphasizes the role of ML in text categorization, particularly in handling large-scale data and improving classification accuracy. The paper concludes by noting the growing importance of text categorization in information retrieval and the need for further research in this area.This paper discusses the application of machine learning (ML) in text categorization, a task of assigning predefined categories to documents. The ML approach involves automatically learning the characteristics of categories from preclassified documents, offering advantages over knowledge engineering methods, such as higher effectiveness and reduced reliance on expert input. The paper reviews key aspects of text categorization, including document representation, classifier construction, and evaluation. It highlights the importance of text categorization in various applications, such as document indexing, filtering, and word sense disambiguation. The paper also discusses the differences between single-label and multi-label categorization, as well as the distinction between document-pivoted and category-pivoted approaches. It emphasizes the role of ML in text categorization, particularly in handling large-scale data and improving classification accuracy. The paper concludes by noting the growing importance of text categorization in information retrieval and the need for further research in this area.