  We present some novel machine learning techniques for the identification of
subcategorization information for verbs in Czech. We compare three different
statistical techniques applied to this problem. We show how the learning
algorithm can be used to discover previously unknown subcategorization frames
from the Czech Prague Dependency Treebank. The algorithm can then be used to
label dependents of a verb in the Czech treebank as either arguments or
adjuncts. Using our techniques, we ar able to achieve 88% precision on unseen
parsed text.
