A tree kernel-based shallow semantic parser for thematic role extraction

Daniele Pighin, Alessandro Moschitti

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

Abstract

We present a simple, two-steps supervised strategy for the identification and classification of thematic roles in natural language texts. We employ no external source of information but automatic parse trees of the input sentences. We use a few attribute-value features and tree kernel functions applied to specialized structured features. Different configurations of our thematic role labeling system took part in 2 tasks of the SemEval 2007 evaluation campaign, namely the closed tasks on semantic role labeling for the English and the Arabic languages. In this paper we present and discuss the system configuration that participated in the English semantic role labeling task and present new results obtained after the end of the evaluation campaign.

Original languageEnglish
Title of host publicationAI IA 2007
Subtitle of host publicationArtificial Intelligence and Human-Oriented Computing - 10th Congress of the Italian Association for Artificial Intelligence, Proceedings
Pages350-361
Number of pages12
Publication statusPublished - 1 Dec 2007
Event10th Congress of the Italian Association for Artificial Intelligence, AI IA 2007 - Rome, Italy
Duration: 10 Sep 200713 Sep 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4733 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th Congress of the Italian Association for Artificial Intelligence, AI IA 2007
CountryItaly
CityRome
Period10/9/0713/9/07

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Pighin, D., & Moschitti, A. (2007). A tree kernel-based shallow semantic parser for thematic role extraction. In AI IA 2007: Artificial Intelligence and Human-Oriented Computing - 10th Congress of the Italian Association for Artificial Intelligence, Proceedings (pp. 350-361). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4733 LNAI).