Katsuya Masuda

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This paper introduces a novel framework for the accurate retrieval of relational concepts from huge texts. Prior to retrieval, all sentences are annotated with predicate argument structures and ontological iden-tifiers by applying a deep parser and a term recognizer. During the run time, user requests are converted into queries of region algebra on these(More)
This paper investigates an application of the ranked region algebra to information retrieval from large scale but unannotated documents. We automatically annotated documents with document structure and semantic tags by using taggers, and retrieve information by specifying structure represented by tags and words using ranked region algebra. We report in(More)
This paper presents a framework for searching text regions with specifying annotated information in tag-annotated text by using Region Algebra. We extend the efficient algorithm for region algebra to handle both nested and crossed regions and introduce variables for attribute values to treat tag-annotations in which attributes indicate another tag regions.(More)
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