Shallow semantic parsing based on FrameNet, VerbNet and PropBank

Ana Maria Giuglea, Alessandro Moschitti

Research output: Chapter in Book/Report/Conference proceedingChapter

9 Citations (Scopus)

Abstract

This article describes a semantic parser based on FrameNet semantic roles that uses a broad knowledge base created by interconnecting three major resources: FrameNet, VerbNet and PropBank. We link the above resources through a mapping between Intersective Levin classes, which are part of PropBank's annotation, and the FrameNet frames. By using Levin classes, we successfully detect FrameNet semantic roles without relying on the frame information. At the same time, the combined usage of the above resources increases the verb coverage and confers more robustness to our parser. The experiments with Support Vector Machines on automatic Levin class detection suggest that (a) tree kernels are well suited for the task and (b) Intersective Levin classes can be used to improve the accuracy of semantic parsing based on FrameNet roles.

Original languageEnglish
Title of host publicationECAI 2006
Subtitle of host publication17th European Conference on Artificial Intelligence August 29 - September 1, 2006, Riva del Garda, Italy
EditorsGerhard Brewka, Silvia Coradeschi, Anna Perini, Paolo Traverso
Pages563-567
Number of pages5
Publication statusPublished - 1 Dec 2006

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume141
ISSN (Print)0922-6389

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ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Giuglea, A. M., & Moschitti, A. (2006). Shallow semantic parsing based on FrameNet, VerbNet and PropBank. In G. Brewka, S. Coradeschi, A. Perini, & P. Traverso (Eds.), ECAI 2006: 17th European Conference on Artificial Intelligence August 29 - September 1, 2006, Riva del Garda, Italy (pp. 563-567). (Frontiers in Artificial Intelligence and Applications; Vol. 141).