Skill Extraction from Job Postings using Weak Supervision

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

doi:10.48550/arXiv.2209.08071

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 chapter › Article in proceedings › Research › peer-review

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.

Original language	English
Title of host publication	RecSys 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.
Publisher	CEUR Workshop Proceedings
Publication date	19 Sept 2022
DOIs	https://doi.org/10.48550/arXiv.2209.08071
Publication status	Published - 19 Sept 2022

Keywords

Labor market demands
Emerging skills
Weak supervision
Skill extraction
Latent representations

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RecSysHR2022-paper_10Final published version, 660 KB

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Zhang, M., Jensen, K. N., van der Goot, R., & Plank, B. (2022). Skill Extraction from Job Postings using Weak Supervision. In RecSys 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. CEUR Workshop Proceedings. https://doi.org/10.48550/arXiv.2209.08071

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title = "Skill Extraction from Job Postings using Weak Supervision",

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.",

keywords = "Labor market demands, Emerging skills, Weak supervision, Skill extraction, Latent representations",

author = "Mike Zhang and Jensen, {Kristian N{\o}rgaard} and {van der Goot}, Rob and Barbara Plank",

year = "2022",

month = sep,

day = "19",

doi = "10.48550/arXiv.2209.08071",

language = "English",

booktitle = "RecSys 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.",

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Zhang, M, Jensen, KN, van der Goot, R & Plank, B 2022, Skill Extraction from Job Postings using Weak Supervision. in RecSys 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.. CEUR Workshop Proceedings. https://doi.org/10.48550/arXiv.2209.08071

Skill Extraction from Job Postings using Weak Supervision. / Zhang, Mike; Jensen, Kristian Nørgaard; van der Goot, Rob et al.
RecSys 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.. CEUR Workshop Proceedings, 2022.

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

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AB - 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.

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KW - Emerging skills

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KW - Skill extraction

KW - Latent representations

U2 - 10.48550/arXiv.2209.08071

DO - 10.48550/arXiv.2209.08071

M3 - Article in proceedings

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ER -

Skill Extraction from Job Postings using Weak Supervision

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Keywords

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