Exploiting syntactic and shallow semantic kernels for question/answer classification

Alessandro Moschitti, Silvia Quarteroni, Roberto Basili, Suresh Manandhar

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

148 Citations (Scopus)

Abstract

We study the impact of syntactic and shallow semantic information in automatic classification of questions and answers and answer re-ranking. We define (a) new tree structures based on shallow semantics encoded in Predicate Argument Structures (PASs) and (b) new kernel functions to exploit the representational power of such structures with Support Vector Machines. Our experiments suggest that syntactic information helps tasks such as question/answer classification and that shallow semantics gives remarkable contribution when a reliable set of PASs can be extracted, e.g. from answers.

Original languageEnglish
Title of host publicationACL 2007 - Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics
Pages776-783
Number of pages8
Publication statusPublished - 1 Dec 2007
Externally publishedYes
Event45th Annual Meeting of the Association for Computational Linguistics, ACL 2007 - Prague, Czech Republic
Duration: 23 Jun 200730 Jun 2007

Other

Other45th Annual Meeting of the Association for Computational Linguistics, ACL 2007
CountryCzech Republic
CityPrague
Period23/6/0730/6/07

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ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

Cite this

Moschitti, A., Quarteroni, S., Basili, R., & Manandhar, S. (2007). Exploiting syntactic and shallow semantic kernels for question/answer classification. In ACL 2007 - Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (pp. 776-783)