This paper 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.