  We present results of two methods for assessing the event profile of news
articles as a function of verb type. The unique contribution of this research
is the focus on the role of verbs, rather than nouns. Two algorithms are
presented and evaluated, one of which is shown to accurately discriminate
documents by type and semantic properties, i.e. the event profile. The initial
method, using WordNet (Miller et al. 1990), produced multiple
cross-classification of articles, primarily due to the bushy nature of the verb
tree coupled with the sense disambiguation problem. Our second approach using
English Verb Classes and Alternations (EVCA) Levin (1993) showed that
monosemous categorization of the frequent verbs in WSJ made it possible to
usefully discriminate documents. For example, our results show that articles in
which communication verbs predominate tend to be opinion pieces, whereas
articles with a high percentage of agreement verbs tend to be about mergers or
legal cases. An evaluation is performed on the results using Kendall's Tau. We
present convincing evidence for using verb semantic classes as a discriminant
in document classification.
