JobBERT: Understanding Job Titles through Skills

• URL: http://arxiv.org/abs/2109.09605v1
• Date: Mon, 20 Sep 2021 15:00:10 GMT
• Title: JobBERT: Understanding Job Titles through Skills
• Authors: Jens-Joris Decorte, Jeroen Van Hautte, Thomas Demeester, Chris Develder
• Abstract summary: Job titles form a cornerstone of today's human resources (HR) processes. Job titles are a compact, convenient, and readily available data source. We propose a neural representation model for job titles, by augmenting a pre-trained language model with co-occurrence information from skill labels extracted from vacancies.
• Score: 12.569546741576515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Job titles form a cornerstone of today's human resources (HR) processes. Within online recruitment, they allow candidates to understand the contents of a vacancy at a glance, while internal HR departments use them to organize and structure many of their processes. As job titles are a compact, convenient, and readily available data source, modeling them with high accuracy can greatly benefit many HR tech applications. In this paper, we propose a neural representation model for job titles, by augmenting a pre-trained language model with co-occurrence information from skill labels extracted from vacancies. Our JobBERT method leads to considerable improvements compared to using generic sentence encoders, for the task of job title normalization, for which we release a new evaluation benchmark.

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