acl acl2013 acl2013-245 acl2013-245-reference knowledge-graph by maker-knowledge-mining
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Author: Hen-Hsen Huang ; Kai-Chun Chang ; Hsin-Hsi Chen
Abstract: This paper aims at understanding what human think in textual entailment (TE) recognition process and modeling their thinking process to deal with this problem. We first analyze a labeled RTE-5 test set and find that the negative entailment phenomena are very effective features for TE recognition. Then, a method is proposed to extract this kind of phenomena from text-hypothesis pairs automatically. We evaluate the performance of using the negative entailment phenomena on both the English RTE-5 dataset and Chinese NTCIR-9 RITE dataset, and conclude the same findings.
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