Automated Resume Evaluation System using NLP
Rohini Nimbekar, Yoqesh Patil, Rahul Prabhu, Shainila Mulla · 2019
Recruiting candidates to fit a particular job profile is a task crucial to most of the companies. Due to increasing growth in online recruitment, traditional hiring methods are becoming inefficient. The conventional techniques usually include a labor-intensive process of manually searching through the applied candidates, reviewing their resumes, and then producing a shortlist of suitable candidates to be interviewed. In this era of technology, job searching has become smarter and more accessible at the same time. The companies receive enormous numbers of resumes/CVs, which are not always structured. There have been lots of work done for the job searching process. Whereas, the process of selecting a candidate based on their resume has not been entirely automated. This research proposes a model of extracting valuable information from the resume and ranking it according to the preference and requirement of the company. To achieve the desired goal, the entire process has been divided into three segments. The first segment consists of converting the unstructured resumes in structured data using NLP, and the second segment consists of the extraction phase, where the relevant information is extracted from the resume and giving them an identifier value. Finally, based on the values assigned, the resumes are ranked accordingly in the final segment.