Making large-scale support vector machine learning practical
Advances in kernel methods
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
A study on convolution kernels for shallow semantic parsing
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
WordNet::Similarity: measuring the relatedness of concepts
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
Measuring the semantic similarity of texts
EMSEE '05 Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment
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In this paper we present a novel similarity between pairs of co-indexed trees to automatically learn textual entailment classifiers. We defined a kernel function based on this similarity along with a more classical intra-pair similarity. Experiments show an improvement of 4.4 absolute percent points over state-of-the-art methods.