Nowadays, the number of people and companies using the Web to search for and advertise job opportunities is growing apace, making data related to the Web labor market a rich source of information for understanding labor market dynamics and trends. In this paper, the emerging term labor market intelligence (LMI) refers to the definition of AI algorithms and frameworks that derive useful knowledge for labor market-related activities, by putting AI into the labor market. At the same time, another branch of AI is developing known as Explainable AI (XAI), whose goal is to obtain interpretable models from current (and future) AI algorithms, given that most of them actually act like black boxes, providing no interpretable explanations of their behavior, as in the case of machine learning. In this paper we connect these two approaches, using a graph model obtained through an NLP-based (Natural Language Processing) methodology for classifying job vacancies. We compare the results obtained with ...

Colace, F., De Santo, M., Lombardi, M., Mercorio, F., Mezzanzanica, M., Pascale, F. (2019). Towards Labour Market Intelligence through Topic Modelling. In Proceedings of the 52nd Hawaii International Conference on System Sciences (pp.5256-5265). IEEE Computer Society.

Towards Labour Market Intelligence through Topic Modelling

Mercorio, F
;
Mezzanzanica, M;
2019

Abstract

Nowadays, the number of people and companies using the Web to search for and advertise job opportunities is growing apace, making data related to the Web labor market a rich source of information for understanding labor market dynamics and trends. In this paper, the emerging term labor market intelligence (LMI) refers to the definition of AI algorithms and frameworks that derive useful knowledge for labor market-related activities, by putting AI into the labor market. At the same time, another branch of AI is developing known as Explainable AI (XAI), whose goal is to obtain interpretable models from current (and future) AI algorithms, given that most of them actually act like black boxes, providing no interpretable explanations of their behavior, as in the case of machine learning. In this paper we connect these two approaches, using a graph model obtained through an NLP-based (Natural Language Processing) methodology for classifying job vacancies. We compare the results obtained with ...
paper
AI, Machine Learning, IoT, and Analytics: Exploring the Implications for Knowledge Management and Innovation Knowledge Innovation and Entrepreneurial Systems Labour Market Intelligence, Explainable Artificial Intelligence, Topic Modelling, Natural Language Processing, Text Classification, Latent Dirichlet Allocation
English
52nd Annual Hawaii International Conference on System Sciences, HICSS 2019
2019
Proceedings of the 52nd Hawaii International Conference on System Sciences
9780998133126
2019
2019-
5256
5265
http://hdl.handle.net/10125/59962
none
Colace, F., De Santo, M., Lombardi, M., Mercorio, F., Mezzanzanica, M., Pascale, F. (2019). Towards Labour Market Intelligence through Topic Modelling. In Proceedings of the 52nd Hawaii International Conference on System Sciences (pp.5256-5265). IEEE Computer Society.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/214174
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