nurlanm/UniSkill_Bert

UniSkill: A Dataset for Matching University Curricula to Professional Competencies

Nurlan Musazade, Joszef Mezei, Mike Zhang

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.

https://aclanthology.org/2026.lrec-1.31/

Citation:

@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." }