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. © Springer-Verlag Berlin Heidelberg 2007.
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) (pp.510-517).
Experimenting a "general purpose" textual entailment learner in AVE
ZANZOTTO, FABIO MASSIMO;MOSCHITTI, ALESSANDRO
2007-01-01
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. © Springer-Verlag Berlin Heidelberg 2007.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
