Leveraging the Inherent Hierarchy of Vacancy Titles for Automated Job Ontology Expansion

• URL: http://arxiv.org/abs/2004.02814v1
• Date: Mon, 6 Apr 2020 16:55:41 GMT
• Title: Leveraging the Inherent Hierarchy of Vacancy Titles for Automated Job Ontology Expansion
• Authors: Jeroen Van Hautte, Vincent Schelstraete, Mika\"el Wornoo
• Abstract summary: We introduce a novel, purely data-driven approach towards the detection of new job titles. Our method is conceptually simple, extremely efficient and competitive with traditional NER-based approaches.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning plays an ever-bigger part in online recruitment, powering intelligent matchmaking and job recommendations across many of the world's largest job platforms. However, the main text is rarely enough to fully understand a job posting: more often than not, much of the required information is condensed into the job title. Several organised efforts have been made to map job titles onto a hand-made knowledge base as to provide this information, but these only cover around 60\% of online vacancies. We introduce a novel, purely data-driven approach towards the detection of new job titles. Our method is conceptually simple, extremely efficient and competitive with traditional NER-based approaches. Although the standalone application of our method does not outperform a finetuned BERT model, it can be applied as a preprocessing step as well, substantially boosting accuracy across several architectures.

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