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.