39 research outputs found
Skill Extraction from Job Postings using Weak Supervision
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.Comment: Accepted in RecSys in HR'22: The 2nd Workshop on Recommender Systems
for Human Resources, in conjunction with the 16th ACM Conference on
Recommender System