Automatic tagging of tasks
| dc.contributor.author | Turganbekov Sh. | |
| dc.date.accessioned | 2024-12-11T06:15:34Z | |
| dc.date.available | 2024-12-11T06:15:34Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | In the modern era, the widespread application of natural language processing has led to a growing need for automated tagging systems. These tags hold significant importance in people’s lives as they aid in search engine optimization and minimize time wastage. The purpose of this research is to address the challenge of automating the tagging process for algorithmic problems. Specifically, our focus lies in developing a machine learning-based approach for accurately assigning difficulty tags to problem descriptions. While acknowledging the limitations of existing machine learning methods, we consider them as a foundational step for future investigations in this domain. By leveraging advanced techniques in natural language processing and machine learning, this study aims to enhance the efficiency and effectiveness of automated task tagging systems. The outcomes of this research can potentially contribute to the development of improved methods for problem classification, facilitating more efficient information retrieval and task prioritization. | |
| dc.identifier.uri | https://repository.sdu.edu.kz/handle/123456789/1565 | |
| dc.language.iso | en | |
| dc.subject | automated tagging systems, time wastage, natural language, machine learning, task prioritization | |
| dc.title | Automatic tagging of tasks | |
| dc.type | Other |