Modeling human inference process for textual entailment recognition
Journal
51st Annual Meeting of the Association for Computational Linguistics
Journal Volume
2
Pages
446-450
ISBN
9781937284510
Date Issued
2013
Author(s)
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. ? 2013 Association for Computational Linguistics.
Type
conference paper