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 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 ...