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

Jeroen Van Hautte, Vincent Schelstraete, Mikaël Wornoo


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.
Anthology ID:
2020.computerm-1.5
Volume:
Proceedings of the 6th International Workshop on Computational Terminology
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Béatrice Daille, Kyo Kageura, Ayla Rigouts Terryn
Venue:
CompuTerm
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
37–42
Language:
English
URL:
https://aclanthology.org/2020.computerm-1.5
DOI:
Bibkey:
Cite (ACL):
Jeroen Van Hautte, Vincent Schelstraete, and Mikaël Wornoo. 2020. Leveraging the Inherent Hierarchy of Vacancy Titles for Automated Job Ontology Expansion. In Proceedings of the 6th International Workshop on Computational Terminology, pages 37–42, Marseille, France. European Language Resources Association.
Cite (Informal):
Leveraging the Inherent Hierarchy of Vacancy Titles for Automated Job Ontology Expansion (Van Hautte et al., CompuTerm 2020)
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PDF:
https://aclanthology.org/2020.computerm-1.5.pdf