End-to-End Resume Parsing and Finding Candidates for a Job Description using BERT

TL;DR

This paper introduces an end-to-end system combining a resume parser and BERT-based ranking to efficiently identify suitable job candidates, achieving high parsing accuracy and promising candidate suitability predictions.

Contribution

It presents a novel integrated approach using BERT for candidate ranking and provides a publicly available resume parser with high accuracy.

Findings

100% accuracy in parsing LinkedIn resumes

73% accuracy in candidate suitability ranking

Publicly available resume parser tool

Abstract

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.