In the contemporary landscape of technological advancement and economic transformation, the proactive identification of emerging skills crucial for the future labor market has become a paramount concern. This paper introduces a Natural Language Processing based methodology for identifying and foresight skills within the Generative Artificial Intelligence domain. We selected three sources to provide a comprehensive view of the domain and to compare the results obtained in the skill foresight exercise from different sources. This comparison was based on conventional measurements like precision and other indicators such as the number of entities extracted for each category and the volume of documents analyzed. The main outputs of this paper are threefold. Firstly, it presents a set of guidelines related to the methodology we designed for skill foresight. Secondly, it includes a practical application of the methodology in the domain of Generative AI to demonstrate how to practically implement the guidelines. Lastly, it offers a comparison of the methodology for different sources, such as job vacancies, course descriptions, and scientific publications, in order to show the potential advantages and drawbacks of NLP when applied to skill foresight on different data sources.
Skill Foresight through Natural Language Processing
vito giordano
;bianca caparrini;antonella martini
2024-01-01
Abstract
In the contemporary landscape of technological advancement and economic transformation, the proactive identification of emerging skills crucial for the future labor market has become a paramount concern. This paper introduces a Natural Language Processing based methodology for identifying and foresight skills within the Generative Artificial Intelligence domain. We selected three sources to provide a comprehensive view of the domain and to compare the results obtained in the skill foresight exercise from different sources. This comparison was based on conventional measurements like precision and other indicators such as the number of entities extracted for each category and the volume of documents analyzed. The main outputs of this paper are threefold. Firstly, it presents a set of guidelines related to the methodology we designed for skill foresight. Secondly, it includes a practical application of the methodology in the domain of Generative AI to demonstrate how to practically implement the guidelines. Lastly, it offers a comparison of the methodology for different sources, such as job vacancies, course descriptions, and scientific publications, in order to show the potential advantages and drawbacks of NLP when applied to skill foresight on different data sources.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.