In recent work, <cite>we</cite> showed (<cite>Zapirain et al., 2009</cite> ) how automatically generated selectional preferences (SP) for verbs were able to perform better than pure lexical features in a role classification experiment, disconnected from a full-fledged SRL system. SPs introduce semantic generalizations on the type of arguments preferred by the predicates and, thus, they are expected to improve results on infrequent and unknown words. The positive effect was especially relevant for out-of-domain data.