TalentCLEF at CLEF2026: Skill and Job Title Intelligence for Human Capital Management

Abstract: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: this https URL

Computation and Language (cs.CL)

arXiv:2607.20009 [cs.CL]

(or arXiv:2607.20009v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.20009 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

Advances in Information Retrieval. ECIR 2026. Lecture Notes in Computer Science, vol 16486. Springer, Cham

https://doi.org/10.1007/978-3-032-21321-1_35 Focus to learn more DOI(s) linking to related resources

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