Today the Web represents a rich source of labour market data for both public and private operators, as a growing number of job offers are advertised through Web portals and services. In this paper we apply and compare several techniques, namely explicit-rules, machine learning, and LDA-based algorithms to classify a real dataset of Web job offers collected from 12 heterogeneous sources against a standard classification system of occupations.
Amato, F., Boselli, R., Cesarini, M., Mercorio, F., Mezzanzanica, M., Moscato, V., et al. (2015). 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). Institute of Electrical and Electronics Engineers Inc. [10.1109/ICOSC.2015.7050852].
Challenge: Processing web texts for classifying job offers
BOSELLI, ROBERTOSecondo
;CESARINI, MIRKO;MERCORIO, FABIO;MEZZANZANICA, MARIO;
2015
Abstract
Today the Web represents a rich source of labour market data for both public and private operators, as a growing number of job offers are advertised through Web portals and services. In this paper we apply and compare several techniques, namely explicit-rules, machine learning, and LDA-based algorithms to classify a real dataset of Web job offers collected from 12 heterogeneous sources against a standard classification system of occupations.
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