Towards explicit soft skills labelling in ESCO through semantic NLP analysis

Ciprian Panzaru () and Anamaria Grama ()
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Ciprian Panzaru: West University of Timisoara
Anamaria Grama: West University of Timisoara

Journal for Labour Market Research, 2025, vol. 59, issue 1, 1-13

Abstract: Abstract The classification and identification of soft skills within the European Skills, Competences, Qualifications, and Occupations (ESCO) framework presents a significant challenge, as ESCO does not explicitly indicate which of its listed skills are considered “soft”. This lack of clear labelling complicates the strategic alignment of educational programs and workforce development initiatives with the evolving demands of today’s labour market, where soft skills are increasingly recognised as essential for professional success. Our study seeks to address this challenge by conducting a systematic literature review to identify and define a comprehensive set of soft skills, and by employing Natural Language Processing (NLP) techniques to map these skills to the ESCO taxonomy. Through semantic analysis and comparison of soft skills identified from scientific literature against the ESCO skillset, we establish a refined classification that explicitly labels soft skills within the ESCO framework. Our findings provide a necessary clarification of soft skills within the standardised European context, offering valuable insights for policymakers, educators, and HR professionals focused on fostering soft skills development. By clarifying the soft skills embedded within the ESCO framework, this research contributes to the advancement of skill taxonomies and promotes a more integrated approach to the development and assessment of soft skills. JEL classification J20, J24, I26, C88.

Keywords: Softs skills; ESCO; NLP; Semantic similarity; Labour market (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1186/s12651-025-00409-x

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