Given a set of WSD problems and their respective labeled examples, we seek to improve overall performance on that set by using all the labeled examples (irrespective of target words) for the entire set in learning a disambiguator for each individual problem.
in effect, on each individual problem (e.g., disambiguation of "art") we benefit from training examples for other problems (e.g., disambiguation of "bar", "canal", and so forth).