Valence Induction with a Head-Lexicalized PCFG
  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. 
Glenn Carroll and Mats Rooth (IMS, Universit\"at Stuttgart)