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.
Challenge: Processing web texts for classifying job offers / Amato, Flora; Boselli, Roberto; Cesarini, Mirko; Mercorio, Fabio; Mezzanzanica, Mario; Moscato, Vincenzo; Persia, Fabio; Picariello, Antonio. - (2015), pp. 460-463. (Intervento presentato al convegno 9th IEEE International Conference on Semantic Computing, IEEE ICSC 2015 tenutosi a Anaheim, United States nel 7 - 9 February, 2015) [10.1109/ICOSC.2015.7050852].
Challenge: Processing web texts for classifying job offers
AMATO, FLORA;MOSCATO, VINCENZO;PICARIELLO, ANTONIO
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
