@InProceedings{do-bethard-moens:2017:I17-1,
  author    = {Do, Quynh Ngoc Thi  and  Bethard, Steven  and  Moens, Marie-Francine},
  title     = {Improving Implicit Semantic Role Labeling by Predicting Semantic Frame Arguments},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {90--99},
  abstract  = {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.},
  url       = {http://www.aclweb.org/anthology/I17-1010}
}