Similarly, an approach which treats the whole predicate-argument structure as an atomic unit (Regneri et al., 2010) will probably fail as well, as such a sparse model is unlikely to be effectively learnable even from large amounts of unlabeled data.
our embedding method would be expected to capture relevant features of the verb frames, namely, the transitive use for the predicate disembark and the effect of the particle away, and these features will then be used by the ranking component to make the correct prediction.