Automating Talent Acquisition Assisted by Resume Parsing System for Enhanced Job Matching

Artificial intelligence has vast potential in resume parsing and human resource management. It aids in proliferating information and data sources, improving software's data management capabilities, and designing intricate and advanced algorithms. Al is changing the way firms hire and users interact with one another. This paper explores the development of a resume parsing system using the RoBERTa model to enhance job matching in talent acquisition. Traditional resume screening processes are time-consuming and prone to human error. Automated systems, although available, often require resumes to follow a specific format. This research presents a novel approach utilizing RoBERTa to parse resumes in various formats, thereby increasing the efficiency and accuracy of the talent acquisition process. The methodology includes the design and implementation of the system, followed by an evaluation of its performance. Results indicate significant improvements in processing time and matching accuracy. The implications of these findings suggest that organizations, regardless of size, can benefit from integrating such a system into their HR processes.