  Implicit semantic role labeling (iSRL) is the task of predicting the semantic
roles of a predicate that do not appear as explicit arguments, but rather
regard common sense knowledge or are mentioned earlier in the discourse. We
introduce an approach to iSRL based on a predictive recurrent neural semantic
frame model (PRNSFM) that uses a large unannotated corpus to learn the
probability of a sequence of semantic arguments given a predicate. We leverage
the sequence probabilities predicted by the PRNSFM to estimate selectional
preferences for predicates and their arguments. On the NomBank iSRL test set,
our approach improves state-of-the-art performance on implicit semantic role
labeling with less reliance than prior work on manually constructed language
resources.
