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Skill Extraction from Job Postings using Weak Supervision

arXiv 2209.08071 ↗First posted 16 Sep 2022cs.CLcited by 13

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Abstract (arXiv)

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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 10 of its 20 references that are in the map. First posted on arXiv 16 Sep 2022.

Builds on

10 of 20 references.

References · 10

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 201975 Languages, 1 Model: Parsing Universal Dependencies Universallycited by 90
  4. 2020Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLPcited by 50
  5. 2021From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understandingcited by 43
  6. 2022SkillSpan: Hard and Soft Skill Extraction from English Job Postingscited by 41
  7. 2021On Cross-Lingual Retrieval with Multilingual Text Encoderscited by 22
  8. 2018Learning Representations for Soft Skill Matchingcited by 19
  9. 2021DataOps for Societal Intelligence: a Data Pipeline for Labor Market Skills Extraction and Matchingcited by 13
  10. 2022Kompetencer: Fine-grained Skill Classification in Danish Job Postings via Distant Supervision and Transfer Learningcited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 5; 2025: 3; 2026: 3.

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  1. 2023ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domaincited by 14
  2. 2024Deep Learning-based Computational Job Market Analysis: A Survey on Skill Extraction and Classification from Job Postingscited by 12
  3. 2023SkillGPT: a RESTful API service for skill extraction and standardization using a Large Language Modelcited by 11
  4. 2024Rethinking Skill Extraction in the Job Market Domain using Large Language Modelscited by 10
  5. 2024MELO: An Evaluation Benchmark for Multilingual Entity Linking of Occupationscited by 6
  6. 2024NNOSE: Nearest Neighbor Occupational Skill Extractioncited by 5
  7. 2024Learning Job Title Representation from Job Description Aggregation Networkcited by 3
  8. 2025Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Managementcited by 3
  9. 2025NLPnorth @ TalentCLEF 2025: Comparing Discriminative, Contrastive, and Prompt-Based Methods for Job Title and Skill Matchingcited by 0
  10. 2025Enhancing Job Matching: Occupation, Skill and Qualification Linking with the ESCO and EQF taxonomiescited by 0
  11. 2026Contrastive Bi-Encoder Models for Multi-Label Skill Extraction: Enhancing ESCO Ontology Matching with BERT and Attention Mechanismscited by 0
  12. 2026Overview of the TalentCLEF 2026: Skill and Job Title Intelligence for Human Capital Managementcited by 0
1 more
  1. 2026TalentCLEF at CLEF2026: Skill and Job Title Intelligence for Human Capital Managementcited by 0