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.
@article{arxiv.1704.02709,
title = {Improving Implicit Semantic Role Labeling by Predicting Semantic Frame Arguments},
author = {Quynh Ngoc Thi Do and Steven Bethard and Marie-Francine Moens},
journal= {arXiv preprint arXiv:1704.02709},
year = {2017}
}