DC Field | Value | Language |
---|---|---|
dc.contributor.author | Na, SH | - |
dc.contributor.author | Kang, IS | - |
dc.contributor.author | Lee, JH | - |
dc.date.accessioned | 2016-04-01T01:40:38Z | - |
dc.date.available | 2016-04-01T01:40:38Z | - |
dc.date.created | 2010-01-11 | - |
dc.date.issued | 2007-07 | - |
dc.identifier.issn | 0306-4573 | - |
dc.identifier.other | 2007-OAK-0000006746 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/23460 | - |
dc.description.abstract | In information retrieval, cluster-based retrieval is a well-known attempt in resolving the problem of term mismatch. Clustering requires similarity information between the documents, which is difficult to calculate at a feasible time. The adaptive document clustering scheme has been investigated by researchers to resolve this problem. However, its theoretical viewpoint has not been fully discovered. In this regard, we provide a conceptual viewpoint of the adaptive document clustering based on query-based similarities, by regarding the user's query as a concept. As a result, adaptive document clustering scheme can be viewed as an approximation of this similarity. Based on this idea, we derive three new query-based similarity measures in language modeling framework, and evaluate them in the context of cluster-based retrieval, comparing with K-means clustering and full document expansion. Evaluation result shows that retrievals based on query-based similarities significantly improve the baseline, while being comparable to other methods. This implies that the newly developed query-based similarities become feasible criterions for adaptive document clustering. (c) 2006 Elsevier Ltd. All rights reserved. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.relation.isPartOf | INFORMATION PROCESSING & MANAGEMENT | - |
dc.subject | adaptive document clustering | - |
dc.subject | query-based similarity | - |
dc.subject | cluster-based retrieval | - |
dc.subject | language modeling approach | - |
dc.title | Adaptive document clustering based on query-based similarity | - |
dc.type | Article | - |
dc.contributor.college | 컴퓨터공학과 | - |
dc.identifier.doi | 10.1016/j.ipm.2006.08.008 | - |
dc.author.google | Na, SH | - |
dc.author.google | Kang, IS | - |
dc.author.google | Lee, JH | - |
dc.relation.volume | 43 | - |
dc.relation.issue | 4 | - |
dc.relation.startpage | 887 | - |
dc.relation.lastpage | 901 | - |
dc.contributor.id | 10083961 | - |
dc.relation.journal | INFORMATION PROCESSING & MANAGEMENT | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCIE | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | INFORMATION PROCESSING & MANAGEMENT, v.43, no.4, pp.887 - 901 | - |
dc.identifier.wosid | 000245605100003 | - |
dc.date.tcdate | 2018-12-01 | - |
dc.citation.endPage | 901 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 887 | - |
dc.citation.title | INFORMATION PROCESSING & MANAGEMENT | - |
dc.citation.volume | 43 | - |
dc.contributor.affiliatedAuthor | Lee, JH | - |
dc.identifier.scopusid | 2-s2.0-33947208411 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 6 | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | adaptive document clustering | - |
dc.subject.keywordAuthor | query-based similarity | - |
dc.subject.keywordAuthor | cluster-based retrieval | - |
dc.subject.keywordAuthor | language modeling approach | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Information Science & Library Science | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Information Science & Library Science | - |
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