Simple fast algorithms for the editing distance between trees and related problems
SIAM Journal on Computing
Machine Learning
A maximum entropy approach to natural language processing
Computational Linguistics
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for the Longest Common Subsequence Problem
Journal of the ACM (JACM)
Machine Learning
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
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
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
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
A survey of paraphrasing and textual entailment methods
Journal of Artificial Intelligence Research
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
An empirical study of recognizing textual entailment in Japanese text
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
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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 another one. In this article, we conduct an empirical study of recognizing textual entailment in Japanese texts, in which we adopt a machine learning-based approach to the task. We quantitatively analyze the effects of various entailment features, machine learning algorithms, and the impact of RTE resources on the performance of an RTE system. This article 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 methods based on lexical matching and syntactic matching. The results also suggest that the machine translation component can be utilized to improve the performance of the RTE system.