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We participated in a complaint and diagnosis task of MedNLP in NTCIR10. We extracted words of complaint/diagnosis by using a hybrid approach with bootstrapping and pattern matching with a medical term dictionary. It was possible that part of the complaint's or diagnosis's expressions are present in the extracted words. Therefore, our system con-catenated(More)
This paper describes NTT DATA's recognizing textual en-tailment(RTE) systems for NTCIR10 RITE2. We participate in four Japanese tasks, BC Subtask, Unit Test, Exam BC and Exam Search[5]. Our approach uses a ratio with the same semantic relations between words. It is necessary to recognize two semantic viewpoints, which are the semantic relation and the(More)
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