Hiroya Susuki

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This paper reports a system constructed for our participation as a group of " keio01 " of Keio University in the T2N (text to number) task at the NTCIR-7 MuST (Multimodal Summarization for Trend Information) task. The constructed system uses newspaper article corpora, task description and domain specific knowledge, and the system outputs chronological(More)
We developed an opinion detection and polarity classification system for Japanese newspapers at NTCIR-7 MOAT task. Our system detects sentences which are " opinionated " or " not opinionated " and classifies them into " positive " , " negative " or " neutral ". We used Support Vector Machines (SVM) as a machine learning method. To determine features, we(More)
This paper reports on an implementation of a question answering system with the vector similarity scoring method. Our question answering system consists of four modules. The question analyzer classifies questions with manually created regular expressions. The document retrieval engine chooses the related articles using the vector space retrieval method. The(More)
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