DC Field | Value | Language |
---|---|---|
dc.contributor.author | Song, Sa-Kwang | ko |
dc.contributor.author | Myaeng, Sung-Hyon | ko |
dc.date.accessioned | 2013-03-12T12:27:02Z | - |
dc.date.available | 2013-03-12T12:27:02Z | - |
dc.date.created | 2012-10-11 | - |
dc.date.created | 2012-10-11 | - |
dc.date.issued | 2012-09 | - |
dc.identifier.citation | INFORMATION PROCESSING & MANAGEMENT, v.48, no.5, pp.919 - 930 | - |
dc.identifier.issn | 0306-4573 | - |
dc.identifier.uri | http://hdl.handle.net/10203/102324 | - |
dc.description.abstract | Term weighting for document ranking and retrieval has been an important research topic in information retrieval for decades. We propose a novel term weighting method based on a hypothesis that a term's role in accumulated retrieval sessions in the past affects its general importance regardless. It utilizes availability of past retrieval results consisting of the queries that contain a particular term, retrieved documents, and their relevance judgments. A term's evidential weight, as we propose in this paper, depends on the degree to which the mean frequency values for the relevant and non-relevant document distributions in the past are different. More precisely, it takes into account the rankings and similarity values of the relevant and non-relevant documents. Our experimental result using standard test collections shows that the proposed term weighting scheme improves conventional TF*IDF and language model based schemes. It indicates that evidential term weights bring in a new aspect of term importance and complement the collection statistics based on TF*IDF. We also show how the proposed term weighting scheme based on the notion of evidential weights are related to the well-known weighting schemes based on language modeling and probabilistic models. (C) 2012 Elsevier Ltd. All rights reserved. | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.subject | INFORMATION | - |
dc.title | A novel term weighting scheme based on discrimination power obtained from past retrieval results | - |
dc.type | Article | - |
dc.identifier.wosid | 000307682100009 | - |
dc.identifier.scopusid | 2-s2.0-84864284059 | - |
dc.type.rims | ART | - |
dc.citation.volume | 48 | - |
dc.citation.issue | 5 | - |
dc.citation.beginningpage | 919 | - |
dc.citation.endingpage | 930 | - |
dc.citation.publicationname | INFORMATION PROCESSING & MANAGEMENT | - |
dc.identifier.doi | 10.1016/j.ipm.2012.03.004 | - |
dc.contributor.localauthor | Myaeng, Sung-Hyon | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Term weighting | - |
dc.subject.keywordAuthor | Evidential weight | - |
dc.subject.keywordAuthor | Discrimination power | - |
dc.subject.keywordAuthor | Language model | - |
dc.subject.keywordAuthor | Probabilistic model | - |
dc.subject.keywordPlus | INFORMATION | - |
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