Classification of Web Job Advertisements: A Case Study

This work is concerned with classifying Web job advertisements against a standard classification system of occupations, by applying 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 classification 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.