Skill Extraction from Job Postings using Weak Supervision
This work addresses the need for efficient skill extraction in labor market analysis, though it appears incremental as it builds on existing weak supervision and taxonomy-based approaches.
The paper tackles the problem of extracting skills from job postings without costly manual annotation by proposing a weakly supervised method that uses the ESCO taxonomy and latent representations, achieving a strong positive signal and outperforming token-level and syntactic baselines.
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.