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In this work, we presented a graph-based semi-supervised system for WSD, based on game theory and consistent labeling principles. Experimental results showed that our method improves the performance of conventional methods and that it requires a small amount of labeled points to outperform supervised systems. These systems require large corpora to be trained. These resources are difficult to create and are not suitable for domain specific tasks. Our system infers the meaning of a target word from a small amount of labeled data exploiting relational and contextual information. In fact, the information of a labeled point is not only used locally by near words but also propagated over the graph and used globally by the dynamical system obtained with our game theoretic framework.


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