In this paper, a supervised learning system of word sense
disambiguation is presented. It is based on \emph{maximum entropy
conditional probability models}. This system acquires the
linguistic knowledge from an annotated corpus and this knowledge
is represented in the form of features. The system were evaluated
both using WordNet's senses and domains as the sets of classes of
each word. Domain labels are obtained from the enrichment of
WordNet with subject field codes which produces a polysemy
reduction. Several types of features has been analyzed for a few
words selected from the DSO corpus. Currently, the system
implementation does not support any smoothing technique or complex
pre-processing but its accuracy of the system is good when it is
compared with, for example, the systems at SENSEVAL-2. Using the
domain enrichment of WordNet, a 14\% of accuracy improvement is
achieved.