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Cited 2 time in webofscience Cited 3 time in scopus
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DC FieldValueLanguage
dc.contributor.authorNa, S.-H-
dc.contributor.authorKang, I.-S-
dc.contributor.authorLee, J.-H.-
dc.date.accessioned2017-07-19T12:31:12Z-
dc.date.available2017-07-19T12:31:12Z-
dc.date.created2014-03-11-
dc.date.issued2008-01-
dc.identifier.issn0302-9743-
dc.identifier.urihttps://oasis.postech.ac.kr/handle/2014.oak/35957-
dc.description.abstractRecently, cluster-based retrieval has been successfully applied to improve retrieval effectiveness. The core part of cluster-based retrieval is inter-document similarities. Although inter-document similarities can be investigated independently of cluster-based retrieval and be further improved in various ways, their direct evaluation has not been seriously considered. Considering that there are many cluster-based retrieval methods, such a direct evaluation method can separate the work of inter-document similarities from the work of cluster-based retrieval. For this purpose, this paper revisits Voorhee's nearest neighbor test as such a direct evaluation, by mainly focusing on whether or not the test is correlated to the retrieval effectiveness. Experimental results consistently verify the use of the nearest neighbor test. As a result, we conclude that the improvement of retrieval effectiveness can be well-predictable from direct evaluation, even without performing runs of cluster-based retrieval.-
dc.languageEnglish-
dc.publisherSPRINGER-
dc.relation.isPartOfLECTURE NOTES IN COMPUTER SCIENCE-
dc.titleREVISIT OF NEAREST NEIGHBOR TEST FOR DIRECT EVALUATION OF INTER-DOCUMENT SIMILARITIES-
dc.typeArticle-
dc.identifier.doi10.1007/978-3-540-78646-7_77-
dc.type.rimsART-
dc.identifier.bibliographicCitationLECTURE NOTES IN COMPUTER SCIENCE, v.4956, pp.674 - 678-
dc.identifier.wosid000254685500074-
dc.date.tcdate2019-03-01-
dc.citation.endPage678-
dc.citation.startPage674-
dc.citation.titleLECTURE NOTES IN COMPUTER SCIENCE-
dc.citation.volume4956-
dc.contributor.affiliatedAuthorLee, J.-H.-
dc.identifier.scopusid2-s2.0-41849089708-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.wostc2-
dc.description.scptc2*
dc.date.scptcdate2018-05-121*
dc.description.isOpenAccessN-
dc.type.docTypeProceedings Paper-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.relation.journalWebOfScienceCategoryComputer Science, Theory & Methods-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-

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이종혁LEE, JONG HYEOK
Grad. School of AI
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