Improving semantic role classification with selectional preferences

Beñat Zapirain, Eneko Agirre, Lluís Marquez, Mihai Surdeanu

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

14 Scopus citations

Abstract

This work incorporates Selectional Preferences (SP) into a Semantic Role (SR) Classification system. We learn separate selectional preferences for noun phrases and prepositional phrases and we integrate them in a state-of-the-art SR classification system both in the form of features and individual class predictors. We show that the inclusion of the refined SPs yields statistically significant improvements on both in domain and out of domain data (14.07% and 11.67% error reduction, respectively). The key factor for success is the combination of several SP methods with the original classification model using meta-classification.

Original languageEnglish (US)
Title of host publicationNAACL HLT 2010 - Human Language Technologies
Subtitle of host publicationThe 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference
Pages373-376
Number of pages4
StatePublished - 2010
Externally publishedYes
Event2010 Human Language Technologies Conference ofthe North American Chapter of the Association for Computational Linguistics, NAACL HLT 2010 - Los Angeles, CA, United States
Duration: Jun 2 2010Jun 4 2010

Publication series

NameNAACL HLT 2010 - Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference

Other

Other2010 Human Language Technologies Conference ofthe North American Chapter of the Association for Computational Linguistics, NAACL HLT 2010
Country/TerritoryUnited States
CityLos Angeles, CA
Period6/2/106/4/10

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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