  This paper presents an experiment in learning valences (subcategorization
frames) from a 50 million word text corpus, based on a lexicalized
probabilistic context free grammar. Distributions are estimated using a
modified EM algorithm. We evaluate the acquired lexicon both by comparison with
a dictionary and by entropy measures. Results show that our model produces
highly accurate frame distributions.
