End-to-End Resume Parsing and Finding Candidates for a Job Description using BERT
September 30, 2019 Β· Declared Dead Β· π arXiv.org
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Authors
Vedant Bhatia, Prateek Rawat, Ajit Kumar, Rajiv Ratn Shah
arXiv ID
1910.03089
Category
cs.IR: Information Retrieval
Citations
47
Venue
arXiv.org
Last Checked
4 months ago
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.
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