Experimenting a "general purpose" textual entailment learner in AVE

Fabio Massimo Zanzotto, Alessandro Moschitti

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

2 Citations (Scopus)

Abstract

In this paper we present the use of a "general purpose" textual entailment recognizer in the Answer Validation Exercise (AVE) task. Our system is designed to learn entailment rules from annotated examples. Its main feature is the use of Support Vector Machines (SVMs) with kernel functions based on cross-pair similarity between entailment pairs. We experimented with our system using different training sets: RTE and AVE data sets. The comparative results show that entailment rules can be learned. Although, the high variability of the outcome prevents us to derive definitive conclusions, the results show that our approach is quite promising and improvable in the future.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages510-517
Number of pages8
Volume4730 LNCS
Publication statusPublished - 1 Dec 2007
Externally publishedYes
Event7th Workshop of the Cross-Language Evaluation Forum, CLEF 2006 - Alicante, Spain
Duration: 20 Sep 200622 Sep 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4730 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other7th Workshop of the Cross-Language Evaluation Forum, CLEF 2006
CountrySpain
CityAlicante
Period20/9/0622/9/06

Fingerprint

Exercise
Support vector machines
Kernel Function
Support Vector Machine
Datasets
Similarity
Training

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Zanzotto, F. M., & Moschitti, A. (2007). Experimenting a "general purpose" textual entailment learner in AVE. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4730 LNCS, pp. 510-517). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4730 LNCS).

Experimenting a "general purpose" textual entailment learner in AVE. / Zanzotto, Fabio Massimo; Moschitti, Alessandro.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4730 LNCS 2007. p. 510-517 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4730 LNCS).

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

Zanzotto, FM & Moschitti, A 2007, Experimenting a "general purpose" textual entailment learner in AVE. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 4730 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4730 LNCS, pp. 510-517, 7th Workshop of the Cross-Language Evaluation Forum, CLEF 2006, Alicante, Spain, 20/9/06.
Zanzotto FM, Moschitti A. Experimenting a "general purpose" textual entailment learner in AVE. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4730 LNCS. 2007. p. 510-517. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Zanzotto, Fabio Massimo ; Moschitti, Alessandro. / Experimenting a "general purpose" textual entailment learner in AVE. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4730 LNCS 2007. pp. 510-517 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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