@inproceedings{musazade-etal-2026-uniskill,
title = "{U}ni{S}kill: A Dataset for Matching University Curricula to Professional Competencies",
author = "Musazade, Nurlan and
Mezei, J{\'o}zsef and
Zhang, Mike",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.31/",
doi = "10.63317/2n39qzvk2eqe",
pages = "456--469",
abstract = "Skill extraction and recommendation systems have been studied from recruiter, applicant, and education perspectives. While AI applications in job advertisements have received broad attention, deficiencies in the instructed skills side remain a challenge. In this work, we address the scarcity of publicly available datasets by releasing both manually annotated and synthetic datasets of skills from the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy and university course pairs and publishing corresponding annotation guidelines. Specifically, we match graduate-level university courses with skills from the Systems Analysts and Management and Organization Analyst ESCO occupation groups at two granularities: course title with a skill, and course sentence with a skill. We train language models on this dataset to serve as a baseline for retrieval and recommendation systems for course-to-skill and skill-to-course matching. We evaluate the models on a portion of the annotated data. Our BERT model achieves 87{\%} F1-score, showing that course and skill matching is a feasible task."
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%0 Conference Proceedings
%T UniSkill: A Dataset for Matching University Curricula to Professional Competencies
%A Musazade, Nurlan
%A Mezei, József
%A Zhang, Mike
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F musazade-etal-2026-uniskill
%X Skill extraction and recommendation systems have been studied from recruiter, applicant, and education perspectives. While AI applications in job advertisements have received broad attention, deficiencies in the instructed skills side remain a challenge. In this work, we address the scarcity of publicly available datasets by releasing both manually annotated and synthetic datasets of skills from the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy and university course pairs and publishing corresponding annotation guidelines. Specifically, we match graduate-level university courses with skills from the Systems Analysts and Management and Organization Analyst ESCO occupation groups at two granularities: course title with a skill, and course sentence with a skill. We train language models on this dataset to serve as a baseline for retrieval and recommendation systems for course-to-skill and skill-to-course matching. We evaluate the models on a portion of the annotated data. Our BERT model achieves 87% F1-score, showing that course and skill matching is a feasible task.
%R 10.63317/2n39qzvk2eqe
%U https://aclanthology.org/2026.lrec-1.31/
%U https://doi.org/10.63317/2n39qzvk2eqe
%P 456-469
Markdown (Informal)
[UniSkill: A Dataset for Matching University Curricula to Professional Competencies](https://aclanthology.org/2026.lrec-1.31/) (Musazade et al., LREC 2026)
ACL