End-to-End Resume Parsing and Finding Candidates for a Job Description using BERT
This addresses the problem of efficient candidate screening for employers, but it is incremental as it applies existing BERT methods to a specific domain.
The paper tackles the challenge of manually screening job applicants by developing an end-to-end system that parses resumes and ranks candidates based on job descriptions using BERT, achieving 100% accuracy on LinkedIn resume parsing and 73% accuracy on candidate suitability.
The ever-increasing number of applications to job positions presents a challenge for employers to find suitable candidates manually. We present an end-to-end solution for ranking candidates based on their suitability to a job description. We accomplish this in two stages. First, we build a resume parser which extracts complete information from candidate resumes. This parser is made available to the public in the form of a web application. Second, we use BERT sentence pair classification to perform ranking based on their suitability to the job description. To approximate the job description, we use the description of past job experiences by a candidate as mentioned in his resume. Our dataset comprises resumes in LinkedIn format and general non-LinkedIn formats. We parse the LinkedIn resumes with 100\% accuracy and establish a strong baseline of 73\% accuracy for candidate suitability.