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

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          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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          Matching resumes and jobs based on relevance models

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            Resume information extraction with cascaded hybrid model

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              Towards an automated system for intelligent screening of candidates for recruitment using ontology mapping (EXPERT)

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                Author and article information

                Journal
                30 September 2019
                Article
                1910.03089
                bb0286d2-6f39-4bde-b4d9-06f70a80110c

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                cs.IR

                Information & Library science
                Information & Library science

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