Skill Extraction from Job Postings using Weak Supervision

TL;DR

This paper introduces a weak supervision method for extracting skills from job postings using a taxonomy-based approach, reducing the need for costly annotations and outperforming traditional pattern-based methods.

Contribution

It presents a novel weak supervision technique leveraging a skills taxonomy to improve skill extraction from job ads without extensive labeled data.

Findings

Outperforms baseline token and pattern-based methods

Leverages European Skills taxonomy for better accuracy

Demonstrates strong positive signal in skill extraction

Abstract

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.