To train CoNLL-specific parse selection models, we parsed the development sections in 500-best mode (using the existing models) and then mechanically 'annotated' the HPSG analyses with maximum DA as preferred, all others as dis-preferred.
this procedure constructs a 'binarized' empirical distribution where estimation of log-linear model parameters amounts to adjusting conditional probabilities towards higher DA values.