  We present a new approach to stochastic modeling of constraint-based grammars
that is based on log-linear models and uses EM for estimation from unannotated
data. The techniques are applied to an LFG grammar for German. Evaluation on an
exact match task yields 86% precision for an ambiguity rate of 5.4, and 90%
precision on a subcat frame match for an ambiguity rate of 25. Experimental
comparison to training from a parsebank shows a 10% gain from EM training.
Also, a new class-based grammar lexicalization is presented, showing a 10% gain
over unlexicalized models.
