Automated Prioritization for Monitoring and Tracking Service Requests using Named Entity Recognition Technique

Service request prioritization in IT service management presents significant challenges in terms of accuracy and efficiency, particularly in Thai-language environments where automated support tools remain limited. This study develops and evaluates a web-based application designed to optimize the management and tracking of organizational service requests. By incorporating automated prioritization through Named Entity Recognition (NER), the system aims to improve operational efficiency and reduce the time staff spend on maintenance-related tasks. In addition to real-time tracking, the application serves as a centralized repository for service request data, enabling in-depth analysis and supporting strategic planning. This capability facilitates more informed decision-making in organizational service management. A key feature of the proposed system is its ability to automatically prioritize incoming service requests based on extracted information. The core component is an NER model fine-tuned using 500 service request samples. Four transformer-based language models, namely BERT-Base-Thai, BERT-Base-Multilingual, WangchanBERTa-Base, and XLM-RoBERTa-Base, were evaluated and compared. The evaluation employed multiple performance metrics, including evaluation loss, precision, recall, F1-score, and accuracy, to determine the most suitable model for the task. Experimental results indicate that BERT-Base-Multilingual achieved the best performance, obtaining an evaluation loss of 0.0272, precision of 0.9888, recall of 0.9946, F1-score of 0.9917, and accuracy of 0.9923. System performance testing further demonstrated that automatic priority assignment required less than 3.5 seconds per request, while service resolution was completed within 48 hours, resulting in an overall user satisfaction score of 4.16 out of 5. The findings confirm the effectiveness of the proposed NER model in accurately identifying multiple entity categories and demonstrate that the developed system successfully enhances service request management while reducing request-tracking time through automated log analysis.