Skill Extraction from Job Postings using Weak Supervision

Mike Zhang, Kristian Nørgaard Jensen, Rob van der Goot, Barbara Plank

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


Aggregated data obtained from job postings provide powerful insights into labor market demands, and emerging skills, and aid job matching. However, most extraction approaches are supervised and thus need costly and time-consuming annotation. To overcome this, we propose Skill Extraction with Weak Supervision. We leverage the European Skills, Competences, Qualifications and Occupations taxonomy to find similar skills in job ads via latent representations. The method shows a strong positive signal, outperforming baselines based on token-level and syntactic patterns.
Original languageEnglish
Title of host publicationRecSys in HR'22: The 2nd Workshop on Recommender Systems for Human Resources, in conjunction with the 16th ACM Conference on Recommender Systems, September 18--23, 2022, Seattle, USA.
PublisherCEUR Workshop Proceedings
Publication date19 Sept 2022
Publication statusPublished - 19 Sept 2022


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