The rapid growth of Web usage for advertising job positions provides a great opportunity for real-time labour market monitoring. This is the aim of Labour Market Intelligence (LMI), a field that is becoming increasingly relevant to EU Labour Market policies design and evaluation. The analysis of Web job vacancies, indeed, represents a competitive advantage to labour market stakeholders with respect to classical survey-based analyses, as it allows for reducing the time-to-market of the analysis by moving towards a fact-based decision making model. In this paper, we present our approach for automatically classifying million Web job vacancies on a standard taxonomy of occupations. We show how this problem has been expressed in terms of text classification via machine learning. Then, we provide details about the classification pipelines we evaluated and implemented, along with the outcomes of the validation activities. Finally, we discuss how machine learning contributed to the LMI needs of the European Organisation that supported the project.

Boselli, R., Cesarini, M., Mercorio, F., Mezzanzanica, M. (2017). Using Machine Learning for Labour Market Intelligence. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part III (pp.330-342). Springer Verlag [10.1007/978-3-319-71273-4_27].

Using Machine Learning for Labour Market Intelligence

Boselli, R.
Co-primo
;
Cesarini, M.
Co-primo
;
Mercorio, F.
Primo
;
Mezzanzanica, M.
Co-primo
2017

Abstract

The rapid growth of Web usage for advertising job positions provides a great opportunity for real-time labour market monitoring. This is the aim of Labour Market Intelligence (LMI), a field that is becoming increasingly relevant to EU Labour Market policies design and evaluation. The analysis of Web job vacancies, indeed, represents a competitive advantage to labour market stakeholders with respect to classical survey-based analyses, as it allows for reducing the time-to-market of the analysis by moving towards a fact-based decision making model. In this paper, we present our approach for automatically classifying million Web job vacancies on a standard taxonomy of occupations. We show how this problem has been expressed in terms of text classification via machine learning. Then, we provide details about the classification pipelines we evaluated and implemented, along with the outcomes of the validation activities. Finally, we discuss how machine learning contributed to the LMI needs of the European Organisation that supported the project.
paper
Governmental application; Machine learning; Text classification;
Machine learning ; Text classification ; Governmental application
English
The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML-PKDD 18-22 september
2017
Boselli, R; Cesarini, M; Mercorio, F; Mezzanzanica, M
Ceci, M; Hollmen, J; Todorovski, L; Vens, C; Džeroski, S
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part III
9783319712727
2017
10536
330
342
http://ecmlpkdd2017.ijs.si/papers/paperID391.pdf
open
Boselli, R., Cesarini, M., Mercorio, F., Mezzanzanica, M. (2017). Using Machine Learning for Labour Market Intelligence. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part III (pp.330-342). Springer Verlag [10.1007/978-3-319-71273-4_27].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/176330
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