Presents comparative evaluations of graph
based word sense disambiguation techniques using several measures of
word semantic similarity and several ranking algorithms. Unsupervised
word sense disambiguation has received a lot of attention lately because
of it's fast execution time and it's ability to make the most of a small
input corpus. Recent state of the art graph based systems have tried to
close the gap between the supervised and the unsupervised approaches.
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