Rethinking Skill Extraction in the Job Market Domain using Large Language Models

Khanh Nguyen, Mike Zhang, Syrielle Montariol, Antoine Bosselut


Abstract
Skill Extraction involves identifying skills and qualifications mentioned in documents such as job postings and resumes. The task is commonly tackled by training supervised models using a sequence labeling approach with BIO tags. However, the reliance on manually annotated data limits the generalizability of such approaches. Moreover, the common BIO setting limits the ability of the models to capture complex skill patterns and handle ambiguous mentions. In this paper, we explore the use of in-context learning to overcome these challenges, on a benchmark of 6 uniformized skill extraction datasets. Our approach leverages the few-shot learning capabilities of large language models (LLMs) to identify and extract skills from sentences. We show that LLMs, despite not being on par with traditional supervised models in terms of performance, can better handle syntactically complex skill mentions in skill extraction tasks.
Anthology ID:
2024.nlp4hr-1.3
Volume:
Proceedings of the First Workshop on Natural Language Processing for Human Resources (NLP4HR 2024)
Month:
March
Year:
2024
Address:
St. Julian’s, Malta
Editors:
Estevam Hruschka, Thom Lake, Naoki Otani, Tom Mitchell
Venues:
NLP4HR | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
27–42
Language:
URL:
https://aclanthology.org/2024.nlp4hr-1.3
DOI:
Bibkey:
Cite (ACL):
Khanh Nguyen, Mike Zhang, Syrielle Montariol, and Antoine Bosselut. 2024. Rethinking Skill Extraction in the Job Market Domain using Large Language Models. In Proceedings of the First Workshop on Natural Language Processing for Human Resources (NLP4HR 2024), pages 27–42, St. Julian’s, Malta. Association for Computational Linguistics.
Cite (Informal):
Rethinking Skill Extraction in the Job Market Domain using Large Language Models (Nguyen et al., NLP4HR-WS 2024)
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PDF:
https://aclanthology.org/2024.nlp4hr-1.3.pdf
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 https://aclanthology.org/2024.nlp4hr-1.3.mp4