At generation time, we find all possible realizations of each attribute value in the input attribute set, and for each combination of attribute realizations, we find the most likely set of dependencies and precedences given the training data.
we bin the selected attribute realizations according to whether they are most likely to precede, depend on and precede, depend on and follow, or follow, the head word they are closest to.