Abstract
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.
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Pham, Q.N.M., Nguyen, L.M., Shimazu, A. (2012). An Empirical Study of Recognizing Textual Entailment in Japanese Text. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2012. Lecture Notes in Computer Science, vol 7181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28604-9_36
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DOI: https://doi.org/10.1007/978-3-642-28604-9_36
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