@inproceedings{van-hautte-etal-2020-leveraging,
    title = "Leveraging the Inherent Hierarchy of Vacancy Titles for Automated Job Ontology Expansion",
    author = {Van Hautte, Jeroen  and
      Schelstraete, Vincent  and
      Wornoo, Mika{\"e}l},
    editor = "Daille, B{\'e}atrice  and
      Kageura, Kyo  and
      Terryn, Ayla Rigouts",
    booktitle = "Proceedings of the 6th International Workshop on Computational Terminology",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.computerm-1.5/",
    pages = "37--42",
    language = "eng",
    ISBN = "979-10-95546-57-3",
    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."
}