Abstract
With the advances in natural language processing and big data analytics, the labor market community has introduced the emerging field of Labor Market Intelligence (LMI). This field aims to design and utilize Artificial Intelligence (AI) algorithms and frameworks to analyze data related to the labor market information for supporting policy and decision-making. This paper elaborates on the automatic classification of free-text Web job vacancies on a standard taxonomy of occupations. In achieving this, we draw on well-established approaches for extracting textual features, which subsequently are employed for training machine learning algorithms. The training and evaluation of our machine learning models were performed with data extracted from online sources, pre-processed, and hand-annotated following the ISCO taxonomy. The results showed that the proposed model is very promising. The advantage is its simplicity. After its application to a relatively small and difficult to clean dataset, it achieved a good accuracy. Furthermore, in this paper we discuss how real-life applications for skill anticipation and matching could benefit from our approach.
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References
Luhn, H.P.: A statistical approach to mechanized encoding and searching of literary information. IBM J. Res. Dev. 1, 309–317 (1957). https://doi.org/10.1147/rd.14.0309
International Labour Office, International Standard Classification of Occupations, International Labour Organization (2012)
Lu, Y., Helou, S. E., Gillet, D.: A recommender system for job seeking and recruiting website. In: Proceedings of the 22nd International Conference on World Wide (2013). https://doi.org/10.1145/2487788.2488092
Shalaby, W., et al.: Help me find a job: a graph-based approach for job recommendation at scale. In: IEEE International Conference on Big Data (BIGDATA) (2017). https://doi.org/10.1109/BigData.2017.8258088
Boselli, R., et al.: WoLMIS: a labor market intelligence system for classifying web job vacancies. J. Intell. Inf. Syst. 51(3), 477–502 (2017). https://doi.org/10.1007/s10844-017-0488-x
Boselli, R., Cesarini, M., Mercorio, F., Mezzanzanica, M.: Using machine learning for labour market intelligence. In: Altun, Y., et al. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10536, pp. 330–342. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71273-4_27
Ikudo, A., Lane, J., Staudt, J., Weinberg, B.: Occupational classifications a machine learning approach. In: IZA - Institute of Labor Economics, no. 11738, August 2018, 9th International Proceedings on Proceedings, pp. 1–2 (2010)
Bethmann, A., Schierholzy, M., Wenzig, K., Zielonka, M.: Automatic coding of occupations. using machine learning algorithms for occupation coding in several german panel surveys. In: Extensible Public Opinion : WAPOR 67th Annual Conference, Nice (2014)
Russ, D.E., et al.: Computer-based coding of free-text job descriptions to efficiently identify occupations in epidemiological studies. Occup. Environ. Med. (2016). https://doi.org/10.1136/oemed-2015-103152
Mukherjee, S., Widmark, D., DiMascio, V., Oates, T.: determining standard occupational classification codes from job descriptions in immigration petitions (2021). arXiv:2110.00078v1, https://doi.org/10.48550/arXiv.2110.00078
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005). https://doi.org/10.1109/TKDE.2005.99
Amato, F., Boselli, R., Cesarini, M., Mercorio, F., Mezzanzanica, M., et al: Classifcation of web job advertisements: a case study. In: SEBD (2015)
Amato, F., Boselli, R., Cesarini, M., Mercorio, F., Mezzanzanica, M., et al: Challenge: processing web texts for classifying job offers. In: Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015), pp. 460–463 (2015). https://doi.org/10.1109/ICOSC.2015.7050852
Marrara, S., et al.: A language modelling approach for discovering novel labour market occupations from the web. In: International Conference on Web Intelligence (2017). https://doi.org/10.1145/3106426.3109035
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: International Conference on Learning Representations (2013). https://doi.org/10.48550/arXiv.1301.3781
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Varelas, G., Lagios, D., Ntouroukis, S., Zervas, P., Parsons, K., Tzimas, G. (2022). Employing Natural Language Processing Techniques for Online Job Vacancies Classification. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022 IFIP WG 12.5 International Workshops. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 652. Springer, Cham. https://doi.org/10.1007/978-3-031-08341-9_27
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DOI: https://doi.org/10.1007/978-3-031-08341-9_27
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