Information Extraction from Resume for Skill Matching

In today's digital age, the efficient matching of candidate skills with job requirements is crucial for optimizing recruitment processes. This paper addresses the challenge of automating skill matching by proposing a novel approach for information extraction from resumes. Drawing upon insights from existing literature and leveraging advancements in natural language processing (NLP) and machine learning, we present a comprehensive methodology that integrates PDF text extraction, custom language model training, and CountVectorizer matching. The research methodology involves assembling a diverse dataset of resumes, training a custom language model on skill extraction tasks, and evaluating the effectiveness of the proposed approach through rigorous experimentation. Our findings demonstrate the efficacy of the proposed methodology in accurately extracting skills from resumes and matching them with job descriptions. The contributions of this research lie in the development of a practical and efficient solution to the challenges of automated skill matching, as well as in advancing the understanding of hybrid approaches in the domain of recruitment technology. This paper serves as a valuable resource for researchers and practitioners seeking to streamline recruitment processes and enhance the alignment between candidate skills and job requirements in the digital era.