2007 RecogTextEntUsingASubseqKernel

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Subject Headings: Textual Entailment

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Abstract

We present a novel approach to RTE that exploits a structure-oriented sentence representation followed by a similarity function. The structural features are automatically acquired from tree skeletons that are extracted and generalized from dependency trees. Our method makes use of a limited size of training data without any external knowledge bases (e.g. WordNet) or handcrafted inference rules. We have achieved an accuracy of 71.1% on the RTE-3 development set performing a 10-fold cross validation and 66.9% on the RTE-3 test data.

References


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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2007 RecogTextEntUsingASubseqKernelRui Wang
Günter Neumann
http://www.aclweb.org/anthology-new/W/W07/W07-1406.pdf Recognizing Textual Entailment Using a Subsequence Kernel Method