In this paper we define a novel similarity measure between examples of textual entailments and we use it as a kernel function in Support Vector Machines (SVMs). This allows us to automatically learn the rewrite rules that describe a non trivial set of entailment cases. The experiments with the data sets of the RTE 2005 challenge show an improvement of 4.4% over the state-of-the-art methods.
Zanzotto, F.m., Moschitti, A. (2006). Automatic learning of textual entailments with cross-pair similarities. In Proceedings of 44th Annual meeting of the Association for computational linguistics (ACL) - (GGS Conference Rating 1 A++) (pp.401-408). Morristown (NJ, USA) : Association for Computational Linguistics [10.3115/1220175.1220226].
Automatic learning of textual entailments with cross-pair similarities
ZANZOTTO, FABIO MASSIMO;MOSCHITTI, ALESSANDRO
2006-01-01
Abstract
In this paper we define a novel similarity measure between examples of textual entailments and we use it as a kernel function in Support Vector Machines (SVMs). This allows us to automatically learn the rewrite rules that describe a non trivial set of entailment cases. The experiments with the data sets of the RTE 2005 challenge show an improvement of 4.4% over the state-of-the-art methods.| File | Dimensione | Formato | |
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