<cite>Hatzivassiloglou and McKeown (1993)</cite> duster adjectives into partitions and present an interesting evaluation to compare the generated adjective classes against those provided by an expert. Their evaluation scheme bases the comparison between two classes on the presence or absence of pairs of words in them. Their approach involves filling in a YES-NO contingency table based on whether a pair of words (adjectives, in their case) is classified in the same class by the human expert and by the system. This method works very well for partitions. However, if it is used to evaluate sets of classes where the classes may be potentiaily overlapping, their technique yields a weaker measure since the same word pair could possibly be present in more than one class. An ideal scheme used to evaluate semantic classes should be able to handle overlapping classes (as o1>. posed to partitions) as well as hierarchies. The technique proposed by Hatzivassiloglou and McKeown does not do a good job of evaluating either of these. In this paper, we present an evaluation methodology which makes it possible to properly evaluate over- In the discussion that follows, the word "clustering" is used to refer to the set of classes that may be either provided by an expert or generated by the system, and the word "class" is used to refer to a single class in the clustering.