Classification of web job advertisements: A case study
Flora Amato, Roberto Boselli, Mirko Cesarini, Fabio Mercorio, Mario Mezzanzanica, Vincenzo Moscato, Fabio Persia, Antonio Picariello · BOA (University of Milano-Bicocca) · 2015
This work is concerned with classifying Web job advertise- ments against a standard classification system of occupations, by apply- ing and comparing different text classification techniques. As a first step, we evaluated the classification algorithms using a hit/not-hit approach, that is either the prediction is correct or not compared to a gold classi- fication provided by domain experts. Then, we built a distance function on top of the affinity relationship between occupations provided by the classification system. Both the classification scores we computed and the affinity distance employed have allowed a more finely grained evaluation of the classified outcomes, providing to authors useful insights towards the improvement of the classification process.