  This work is a study of the impact of multiple aspects in a classic
unsupervised word sense disambiguation algorithm. We identify relevant factors
in a decision rule algorithm, including the initial labeling of examples, the
formalization of the rule confidence, and the criteria for accepting a decision
rule. Some of these factors are only implicitly considered in the original
literature. We then propose a lightly supervised version of the algorithm, and
employ a pseudo-word-based strategy to evaluate the impact of these factors.
The obtained performances are comparable with those of highly optimized
formulations of the word sense disambiguation method.
