Chinese-SkillSpan: A Span-Level Dataset for ESCO-Aligned Competency Extraction from Chinese Job Ads

2026-04-24

AI Summary

This study addresses the scarcity of high-quality skill entity recognition datasets in Chinese job postings, which hinders the advancement of intelligent employment services. To bridge this gap, the authors construct Chinese-SkillSpan, the first Chinese job-skill named entity recognition dataset, and introduce a four-dimensional annotation framework—encompassing knowledge, skills, general competencies, and language abilities—aligned with the international ESCO occupational standard. They further design a large language model–driven macro-micro collaborative annotation pipeline, enabling efficient labeling of over 20,000 job advertisements collected from four major platforms spanning 2014 to 2025. This work fills a critical resource void in Chinese skill extraction and establishes a benchmark dataset and novel paradigm for research in intelligent recruitment.

Technology Category

• Natural Language Processing: Information Extraction
• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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 JobSkillNER dataset aligned with the ESCO occupational skill standard across four dimensions: knowledge, skill, transversal competence, and language competence (LSKT). Experimental results show that the dataset supports effective model training and evaluation, indicating that Chinese-SkillSpan helps fill a major gap in Chinese JobSkillNER resources and provides a useful benchmark for intelligent recruitment research. Code and data are available at https://sites.google.com/view/cn-skillspan-resources .

Problem

• Job Skill Named Entity Recognition
• Chinese job ads
• ESCO
• competency extraction
• span-level dataset

Innovation

• JobSkillNER
• Chinese-SkillSpan
• ESCO alignment
• LLM-empowered annotation
• span-level dataset