用NLP提升人才匹配与技能识别，支持跨语言多行业的人力资源管理。
TalentCLEF at CLEF2026: Skill and Job Title Intelligence for Human Capital Management
- 构建双任务评测框架，聚焦岗位-人与岗位-技能的精准匹配。
- 支持隐私保护的数据上下文检索，提升候选人推荐准确性。
- 适合关注人力资源智能化、公平性与多语言NLP的研究者。
本文介绍TalentCLEF挑战赛的第二届，作为CLEF 2026的评测实验室运行。该挑战旨在推动自然语言处理（NLP）在人力资本管理（HCM）领域的应用，鼓励开发具备公平性、多语言支持和跨行业适应性的系统。为此，TalentCLEF设立公开基准，供研究团队比较方法并共享成果，促进更实用、有影响力的NLP解决方案落地。今年的评测包含两项任务：(i) Task A - 上下文感知的岗位-人选匹配，利用丰富上下文和隐私保护数据检索并排序合适候选人；(ii) Task B - 岗位-技能匹配与技能类型分类，针对特定职位名称识别相关技能并按类型归类。项目官网：https://talentclef.github.io/talentclef/
原文摘要 · Abstract (English)
This paper presents the second edition of the TalentCLEF Challenge, which will run as an evaluation lab as part of CLEF 2026. The aim of TalentCLEF is to promote the development of systems and methods that use Natural Language Processing (NLP) in the field of Human Capital Management (HCM), fostering approaches that ensure fairness in results, operate across multiple languages, and adapt to diverse industries. To this end, TalentCLEF establishes public benchmarks where research teams can compare methods and share findings, moving the field toward more practical and impactful NLP solutions that effectively address the real needs of workforce management. This year's lab will feature two tasks designed to foster the development and evaluation of systems that support key HCM activities such as talent matching, upskilling, reskilling, and skill gap detection: (i) Task A - Contextualized Job-Person Matching, focused on retrieving and ranking suitable candidates for specific job positions using context-rich and privacy-preserving data; and (ii) Task B - Job-Skill Matching with Skill Type Classification, centered on identifying relevant skills for a given job title and classifying them by their type within the job profile. TalentCLEF website: https://talentclef.github.io/talentclef/
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