发表机构
Avature Machine Learning; NLP & IR Group at UNED（阿瓦图尔机器学习公司; 西班牙国立远程教育大学自然语言处理与信息检索组）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
TalentCLEF挑战赛第二版作为CLEF 2026评估实验室，旨在推动HCM领域NLP发展。今年设两项任务，任务A是情境化职位与人匹配，任务B是带技能类型分类的职位与技能匹配，通过建立公共基准促进相关系统和方法发展。
AI 中文摘要
本文介绍了TalentCLEF挑战赛的第二版，它将作为CLEF 2026的评估实验室运行。TalentCLEF旨在推动人力资本管理（HCM）领域中使用自然语言处理（NLP）的系统和方法的发展，培养能确保结果公平、跨多种语言运行并适应不同行业的方法。为此，TalentCLEF建立了公共基准，让研究团队能比较方法和分享成果。今年的实验室有两项任务：任务A是情境化的职位与人匹配，用丰富且保护隐私的数据检索和排名适合特定职位的候选人；任务B是带有技能类型分类的职位与技能匹配，为给定职位识别相关技能并按其在职位描述中的类型分类。
英文摘要
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/
Journal refAdvances in Information Retrieval. ECIR 2026. Lecture Notes in Computer Science, vol 16486. Springer, Cham
DOI：10.1007/978-3-032-21321-1_35
