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dc.contributor.authorSalton, Gerarden_US
dc.contributor.authorBuckley, Chrisen_US
dc.date.accessioned2007-04-23T17:49:56Z
dc.date.available2007-04-23T17:49:56Z
dc.date.issued1990-09en_US
dc.identifier.citationhttp://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR90-1158en_US
dc.identifier.urihttps://hdl.handle.net/1813/6998
dc.description.abstractVery large text databases now exist in machine-readable form, covering arbitrary subject matter in unrestricted discourse areas. The conventional text retrieval approaches are not easily used in such circumstances, because the knowledge needed to understand unrestricted subject matter is not readily available for practical use. A new approach is outlined for text structuring and retrieval, based on flexible text matching methods using different context granularities. When global as well as local similarities exist between distinct texts, the presumption is that the texts cover semantically similar subject areas. This leads to the automatic introduction of links between related texts, and to the retrieval of text excerpts in response to available user queries. Evaluation results are given to demonstrate the effectiveness of the text matching approach.en_US
dc.format.extent2615739 bytes
dc.format.extent635427 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.language.isoen_USen_US
dc.publisherCornell Universityen_US
dc.subjectcomputer scienceen_US
dc.subjecttechnical reporten_US
dc.titleFlexible Text Matching for Information Retrievalen_US
dc.typetechnical reporten_US


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