HR-Specific NLP for the Homogeneous Classification of Declared and Inferred Skills

ABSTRACT

The use of natural language processing in human resource management has become of paramount importance in order to provide support for recruiting and corporate population management. This paper proposes a heuristic algorithm to solve two problems: (i) semantic matching among heterogeneous datasets storing the hard skills possessed by the company’s employees to obtain a homogeneous catalog, according to the O*NET and ESCO competence dictionaries, and (ii) inferring the employee’s soft skills with respect to his/her own declaration of interests, work experience, certifications, etc., given his/her curriculum vitae. Empirical results demonstrate that the proposed approach yields improved performance results by comparison with baseline methods available in the literature.

Acknowledgements

The authors thank Ing. A Valli for the fruitful discussions.

Disclosure statement

The work presented in this paper was carried out while Ing. F. Kieffer and Ing. V. Paduano were with ELIS Innovation Hub and does not reflect the results of any activity carried out at Generali Assicurazioni and Storm Reply – CNA, to which the two above-mentioned authors are currently affiliated, respectively. No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was supported by ‘Consel–Consorzio ELIS per la formazione professionale superiore’ within the framework of the 2020 Joint Research Project initiative funded by Enel S.p.A. This research was also partially supported by the European Commission in the framework of the Erasmus+ I4EU project (Key competences for a European model of Industry 4.0) under Grant Agreement no. 2019-1-FR01-KA202-062965.