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 publicationEvaluation of Multilingual and Multi-modal Information Retrieval - 7th Workshop of the Cross-Language Evaluation Forum, CLEF 2006, Revised Selected Papers
Pages510-517
Number of pages8
Publication statusPublished - 1 Dec 2007
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)0302-9743
ISSN (Electronic)1611-3349

Other

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

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Zanzotto, F. M., & Moschitti, A. (2007). Experimenting a "general purpose" textual entailment learner in AVE. In Evaluation of Multilingual and Multi-modal Information Retrieval - 7th Workshop of the Cross-Language Evaluation Forum, CLEF 2006, Revised Selected Papers (pp. 510-517). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4730 LNCS).