TalentCLEF 2026推动人才匹配技术发展，聚焦岗位与技能智能识别。
Overview of the TalentCLEF 2026: Skill and Job Title Intelligence for Human Capital Management
- 构建双任务评估框架：岗位-人选匹配与岗位-技能匹配。
- 覆盖英西双语，支持核心/上下文技能类型分类。
- 吸引113支团队参与，400+提交，推动人力资源NLP研究
本文概述了在CLEF 2026会议上举办的第二届TalentCLEF挑战赛。该挑战赛旨在推动人力资源管理领域自然语言处理技术的发展。第二届时段包含两项任务：任务A为上下文化岗位-人选匹配，旨在针对英文和西班牙语的职位空缺，从简历中识别并排序最合适的候选人；任务B为岗位-技能匹配并进行技能类型分类，目标是为给定职位标题检索最相关技能，并区分核心技能与上下文技能。本次挑战吸引了113支注册队伍，共提交超过400份结果，反映出学术界对人力资源管理共享评估基准日益增长的兴趣。本文介绍了挑战赛的动机与组织方式，总结了数据集与评估设置，并报告了参赛团队的主要成果。
原文摘要 · Abstract (English)
This paper presents an overview of the second edition of the TalentCLEF challenge, organized as a Lab at the Conference and Labs of the Evaluation Forum (CLEF) 2026. TalentCLEF is an initiative aimed at advancing Natural Language Processing research in Human Capital Management. The second edition of the challenge consisted of two tasks: Task A, contextualized job-person matching, focuses on identifying and ranking the most suitable candidates represented by their resumes for a given job vacancy in English and Spanish. Task B, job-skill matching with skill type classification, addresses retrieving the most relevant skills for a given job title in English and distinguishing between core and contextual skills. TalentCLEF attracted 113 registered teams and received more than 400 submissions in the two tasks, reflecting the growing interest of the research community in shared evaluation benchmarks for Human Capital Management. This paper describes the motivation and organization of the challenge, summarizes the datasets and evaluation settings, and reports the main results obtained by the participating teams.
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