Improving Implicit Semantic Role Labeling by Predicting Semantic Frame Arguments

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

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.

Original languageEnglish (US)
Title of host publication8th International Joint Conference on Natural Language Processing - Proceedings of the IJCNLP 2017, System Demonstrations
PublisherAssociation for Computational Linguistics (ACL)
Pages90-99
Number of pages10
ISBN (Electronic)9781948087025
StatePublished - 2017
Event8th International Joint Conference on Natural Language Processing, IJCNLP 2017 - Taipei, Taiwan, Province of China
Duration: Nov 27 2017Dec 1 2017

Publication series

Name8th International Joint Conference on Natural Language Processing - Proceedings of the IJCNLP 2017, System Demonstrations
Volume1

Conference

Conference8th International Joint Conference on Natural Language Processing, IJCNLP 2017
Country/TerritoryTaiwan, Province of China
CityTaipei
Period11/27/1712/1/17

ASJC Scopus subject areas

  • Language and Linguistics
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Software

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