Job titles form a cornerstone of today's human resources (HR) processes. Within online recruitment, they allow candidates to understand the contents of a vacancy at a glance, while internal HR departments use them to organize and structure many of their processes. As job titles are a compact, convenient, and readily available data source, modeling them with high accuracy can greatly benefit many HR tech applications. In this paper, the authors propose a neural representation model for job titles, by augmenting a pre-trained language model with co-occurrence information from skill labels extracted from vacancies. JobBERT method leads to considerable improvements compared to using generic sentence encoders, for the task of job title normalisation, for a new evaluation benchmark was released. 

In order to evaluate the performance of our model, the authors created and published a new dataset of vacancy titles that are labeled with the standardised ESCO occupations. This data was gathered from a large governmental online job board, on which each job posting is tagged with the most suitable ESCO occupation label by its creator.

Read the full study: "JobBERT: Understanding Job Titles through Skills"

This article contributes to the broader collection of external ESCO publications, showcasing the use of ESCO within various methodologies or its presentation in both European and International contexts. As ESCO becomes increasingly used in applications and research projects across Europe and beyond, it is valuable to collect such sources and share best practices by diverse stakeholders. Therefore, this collection of external publications strengthens the exchange of knowledge within the ESCO community and can contribute to mutual learning in the field of skills, occupations and qualifications among European and international actors. If you are interested in sharing your publication, please write to EMPL-ESCO-SECRETARIAT@ec.europa.eu