  Acquiring lexical information is a complex problem, typically approached by
relying on a number of contexts to contribute information for classification.
One of the first issues to address in this domain is the determination of such
contexts. The work presented here proposes the use of automatically obtained
FORMAL role descriptors as features used to draw nouns from the same lexical
semantic class together in an unsupervised clustering task. We have dealt with
three lexical semantic classes (HUMAN, LOCATION and EVENT) in English. The
results obtained show that it is possible to discriminate between elements from
different lexical semantic classes using only FORMAL role information, hence
validating our initial hypothesis. Also, iterating our method accurately
accounts for fine-grained distinctions within lexical classes, namely
distinctions involving ambiguous expressions. Moreover, a filtering and
bootstrapping strategy employed in extracting FORMAL role descriptors proved to
minimize effects of sparse data and noise in our task.
