Skill Extraction

Skill extraction focuses on automatically identifying skills and competencies from textual data, such as job postings and resumes, to understand labor market demands and improve job matching. Current research emphasizes the use of large language models (LLMs) and traditional named entity recognition (NER) methods, often incorporating techniques like weak supervision, contrastive learning, and retrieval augmentation to overcome data scarcity and annotation challenges. This field is crucial for analyzing labor market trends, improving recruitment processes, and informing the design of educational and career guidance systems.