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About this paper
Jens-Joris Decorte, Jeroen Van Hautte, Thomas Demeester, Chris Develder
Researchers at Ghent University and TechWolf developed JobBERT, a model that learns semantic representations of job titles by distantly supervising with associated skills extracted from online vacancies. This approach enables flexible job title normalization against any taxonomy, achieving superior performance in Mean Reciprocal Rank and Recall@k compared to leading semantic text similarity encoders.
Key insights
- Distant supervision, leveraging readily available skill data from online vacancies, effectively bypasses the high annotation costs and static taxonomy dependence of traditional job title normalization methods.
- Learning job title representations based on their associated skills enables dynamic and flexible normalization against any job taxonomy, which is crucial for adapting to evolving market needs.
- A gating mechanism within the BERT encoder successfully identifies and prioritizes job-relevant tokens within a title, leading to higher quality semantic embeddings by downplaying irrelevant information.
Summary by alphaXiv.
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