In Natural Language Processing, entity linking (EL) has centered around Wikipedia, but remains underexplored for the job market domain. Disambiguating skill mentions can help the researchers to get insight into the labor market demands. In this work, they are the first to explore EL in this domain, specifically targeting the linkage of occupational skills to the ESCO taxonomy (le Vrang et al., 2014). Previous efforts linked coarse-grained (full) sentences to a corresponding ESCO skill. In this work, they link more fine-grained span-level mentions of skills. The researchers tune two high-performing neural EL models, a bi-encoder (Wu et al., 2020) and an autoregressive model (Cao et al., 2021), on a synthetically generated mention– skill pair dataset and evaluate them on a human annotated skill-linking benchmark. The findings reveal that both models are capable of linking implicit mentions of skills to their correct taxonomy counterparts. Empirically, BLINK outperforms GENRE in strict evaluation, but GENRE performs better in loose evaluation.

Read the full study: "Entity Linking in the Job Market Domain"

This article contributes to the broader collection of external ESCO publications, showcasing the use of ESCO within various methodologies or its presentation in both European and International contexts. As ESCO becomes increasingly used in applications and research projects across Europe and beyond, it is valuable to collect such sources and share best practices by diverse stakeholders. Therefore, this collection of external publications strengthens the exchange of knowledge within the ESCO community and can contribute to mutual learning in the field of skills, occupations and qualifications among European and international actors. If you are interested in sharing your publication, please write to EMPL-ESCO-SECRETARIAT@ec.europa.eu