Automatic Text Categorization Using Semantic Dictionaries

Abstract

In the modern information society, a huge amount of textual content becomes available to the user every day. In such conditions, the relevance and effectiveness of information presentation become critically important. People spend more and more time searching for and filtering information to find what they really need. One solution to this problem can be text categorization, which organizes information into a structured form, making it much easier to search and comprehend. The reader can quickly scan the categories and select the topic of interest, saving time and improving information perception. In some areas, such as news, science, or technology, where the relevance of information is extremely important, categorization allows quickly finding the necessary information and staying up-to-date with the latest events and developments in a specific field. This is especially useful for professionals, researchers, and active users who need to stay informed. Finally, text categorization contributes to improving user experience and satisfying readers' needs. It helps reduce the time spent on searching and reading text, as well as increases the level of understanding and assimilation of information. This can lead to increased user satisfaction and their return to the source of information in the future. Therefore, the relevance of automatic text categorization is quite high.