This paper presents the introduction of WordNet semantic classes in a dependency parser, obtaining improvements on the full Penn Treebank for the first time. We tried different combinations of some basic se-mantic classes and word sense disambigua-tion algorithms. Our experiments show that selecting the adequate combination of se-mantic features on development data is key for success. Given the basic nature of the semantic classes and word sense disam-biguation algorithms used, we think there is ample room for future improvements.