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        <title>Leveraging the Inherent Hierarchy of Vacancy Titles for Automated Job Ontology Expansion</title>
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    <name type="personal">
        <namePart type="given">Jeroen</namePart>
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    <name type="personal">
        <namePart type="given">Vincent</namePart>
        <namePart type="family">Schelstraete</namePart>
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    <name type="personal">
        <namePart type="given">Mikaël</namePart>
        <namePart type="family">Wornoo</namePart>
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            <title>Proceedings of the 6th International Workshop on Computational Terminology</title>
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            <namePart type="given">Béatrice</namePart>
            <namePart type="family">Daille</namePart>
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            <namePart type="given">Ayla</namePart>
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            <namePart type="family">Terryn</namePart>
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            <publisher>European Language Resources Association</publisher>
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    <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.</abstract>
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%0 Conference Proceedings
%T Leveraging the Inherent Hierarchy of Vacancy Titles for Automated Job Ontology Expansion
%A Van Hautte, Jeroen
%A Schelstraete, Vincent
%A Wornoo, Mikaël
%Y Daille, Béatrice
%Y Kageura, Kyo
%Y Terryn, Ayla Rigouts
%S Proceedings of the 6th International Workshop on Computational Terminology
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-57-3
%G English
%F van-hautte-etal-2020-leveraging
%X 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.
%U https://aclanthology.org/2020.computerm-1.5
%P 37-42

Markdown (Informal)

[Leveraging the Inherent Hierarchy of Vacancy Titles for Automated Job Ontology Expansion](https://aclanthology.org/2020.computerm-1.5) (Van Hautte et al., CompuTerm 2020)

• Leveraging the Inherent Hierarchy of Vacancy Titles for Automated Job Ontology Expansion (Van Hautte et al., CompuTerm 2020)

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.