Algorithms for the Longest Common Subsequence Problem
Journal of the ACM (JACM)
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Japanese dependency analysis using cascaded chunking
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Extracting semantic orientations of words using spin model
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Methods for using textual entailment in open-domain question answering
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Learning textual entailment using SVMs and string similarity measures
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Natural language inference
Towards cross-lingual textual entailment
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Using bilingual parallel corpora for cross-lingual textual entailment
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
The PASCAL recognising textual entailment challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
ACM Transactions on Asian Language Information Processing (TALIP) - Special Issue on RITE
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Recognizing Textual Entailment (RTE) is a fundamental task in Natural Language Understanding. The task is to decide whether the meaning of a text can be inferred from the meaning of the other one. In this paper, we conduct an empirical study of the RTE task for Japanese, adopting a machine-learning-based approach. We quantitatively analyze the effects of various entailment features and the impact of RTE resources on the performance of a RTE system. This paper also investigates the use of Machine Translation for the RTE task and determines whether Machine Translation can be used to improve the performance of our RTE system. Experimental results achieved on benchmark data sets show that our machine-learning-based RTE system outperforms the baseline method based on lexical matching. The results also suggest that the Machine Translation component can be utilized to improve the performance of the RTE system.