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
Venues:
CompuTerm | LREC | WS
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
37–42
Language:
English
URL:
https://www.aclweb.org/anthology/2020.computerm-1.5
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.computerm-1.5.pdf