Chinese-SkillSpan: A Span-Level Dataset for ESCO-Aligned Competency Extraction from Chinese Job Ads
Published in arXiv preprint arXiv:2604.23009, 2026
Job Skill Named Entity Recognition (JobSkillNER) aims to automatically extract key skill information from large-scale job posting data, which is important for improving talent-market matching efficiency and supporting personalized employment services. To the best of our knowledge, this work presents the first Chinese JobSkillNER dataset for recruitment texts. We propose annotation guidelines tailored to Chinese job postings and an LLM-empowered Macro-Micro collaborative annotation pipeline. The pipeline leverages the contextual understanding ability of large language models (LLMs) for initial annotation and then refines the results through expert sentence-level adjudication. Using this pipeline, we annotate more than 20,000 instances collected from four major recruitment platforms over the period 2014–2025. Based on these efforts, we release Chinese-SkillSpan, the first Chinese job-skill dataset aligned with the ESCO competency taxonomy.
Citation: Guojing Li, Zichuan Fu, Junyi Li, Wenxia Zhou, Xinyang Wu, Jinning Yang, Jingtong Gao, Feng Huang, and Xiangyu Zhao. 2026. Chinese-SkillSpan: A Span-Level Dataset for ESCO-Aligned Competency Extraction from Chinese Job Ads. arXiv preprint arXiv:2604.23009. arXiv:2604.23009
Download Paper