Our experiments show that the large amount of knowledge injected into WordNet is of high quality and, more importantly, it enables simple knowledge-based WSD systems to perform as well as the highest-performing supervised ones in a coarse-grained setting and to outperform them on domain-specific text.
our results go one step beyond previous findings (Cuadros and Rigau, 2006;Navigli and Lapata, 2010) and prove that knowledge-rich disambiguation is a competitive alternative to supervised systems, even when relying on a simple algorithm.