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This paper describes Japanese textual entailment recognition systems for NTCIR-10 RITE2. The tasks that we participated in are the Japanese BC subtask and the Ex-amBC subtask. Our methods are based on some machine learning techniques with surface level, syntax and semantic features. We use two ontologies, the Japanese WordNet and Nihongo-Goi-Taikei, and(More)
In spite of wide-ranging previous studies on synthetic macrocycles, the installation of open-close functions into the frameworks remains a challenge. We present a new polyaromatic macrocycle capable of switching between open and closed forms in response to external stimuli, namely, base and acid. The macrocycle, which is prepared in three steps, has a(More)
In this paper, we propose a method for extracting trouble information from Twitter. One useful approach is based on machine learning techniques such as SVMs. However, trouble information is a fraction of a percent of all tweets on Twitter. In general, imbalanced distribution is not suitable for machine learning techniques to generate a classifier. Another(More)
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