Leveraging the Inherent Hierarchy of Vacancy Titles for Automated Job Ontology Expansion
This work addresses the challenge of automated job ontology expansion for online recruitment platforms, though it is incremental as it builds on existing methods.
The paper tackled the problem of incomplete coverage in job title mapping for online recruitment by introducing a data-driven method for detecting new job titles, which, when used as a preprocessing step, substantially boosts accuracy across several architectures.
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.