Many languages lack broad-coverage, semantically annotated lexical resources, which limits empirical research on lexical semantics for these languages. In this paper, we report on how we automatically enriched the French Wiktionnary with general semantic classes, known as supersenses, using a limited amount of manually annotated data. We trained a classifier combining sense definition classification and sense exemplars classification. The resulting resource, with an evaluated supersense accuracy of nearly 85% (92% for hypersenses), is used in a case study illustrating how such an semantically enriched resource can be leveraged to empirically test linguistic hypotheses about the lexicon, on a large scale.