1. An Empirical Study of Recognizing Textual Entailment in Japanese Text

    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 ...
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      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD
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