We thus chose to apply feature-rich EM (Berg-Kirkpatrick et al., 2010) in GUSP, which enabled the use of more generalizable features.
gUSP defines a probability distribution over dependency tree d and semantic parse z by where f i and w i are features and their weights, and Z is the normalization constant that sums over all possible d, z (over the same unlabeled tree).