These approaches have shown good results; particularly those using supervised learning (see Mihalcea et al., 2004 for an overview of state-ofthe-art systems).
current approaches rely on limited knowledge representation and modeling techniques: traditional machine learning algorithms and attribute-value vectors to represent disambiguation instances.