DC Field | Value | Language |
---|---|---|
dc.contributor.author | Na, SH | - |
dc.contributor.author | Kang, IS | - |
dc.contributor.author | Lee, JH | - |
dc.date.accessioned | 2016-04-01T02:12:36Z | - |
dc.date.available | 2016-04-01T02:12:36Z | - |
dc.date.created | 2010-01-11 | - |
dc.date.issued | 2005-01 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.other | 2005-OAK-0000005021 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/24662 | - |
dc.description.abstract | Recently, researchers have tried to extend a language modeling approach to apply relevance feedback. Their approaches can be classified into two categories. One typical approach is the expansion-based feedback that sequentially performs 'term selection' and 'term re-weighting' separately. Another approach is the model-based feedback that focuses on estimating 'query language model', which predicts well users' information need. This paper improves these two approaches of relevance feedback by using a maximum a posteriori probability criterion, and a three-component mixture model. A maximum a posteriori probability criterion is a criterion for selection of good expansion terms from feedback documents. A three-component mixture model is the method that eliminates the noise of the query language model by adding a 'document specific topic model'. The experimental results show that our methods increase the precision of relevance feedback for a short length query. In addition, we make some comparative study between several relevance feedbacks in three document collections. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | SPRINGER-VERLAG BERLIN | - |
dc.relation.isPartOf | LECTURE NOTES IN COMPUTER SCIENCE | - |
dc.title | Improving relevance feedback in language modeling approach: Maximum a posteriori probability criterion and three-component mixture model | - |
dc.type | Article | - |
dc.contributor.college | 컴퓨터공학과 | - |
dc.identifier.doi | 10.1007/978-3-540-30211-7_14 | - |
dc.author.google | Na, SH | - |
dc.author.google | Kang, IS | - |
dc.author.google | Lee, JH | - |
dc.relation.volume | 3248 | - |
dc.relation.startpage | 130 | - |
dc.relation.lastpage | 138 | - |
dc.contributor.id | 10083961 | - |
dc.relation.journal | LECTURE NOTES IN COMPUTER SCIENCE | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCIE | - |
dc.collections.name | Conference Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | LECTURE NOTES IN COMPUTER SCIENCE, v.3248, pp.130 - 138 | - |
dc.identifier.wosid | 000228359800014 | - |
dc.date.tcdate | 2018-03-23 | - |
dc.citation.endPage | 138 | - |
dc.citation.startPage | 130 | - |
dc.citation.title | LECTURE NOTES IN COMPUTER SCIENCE | - |
dc.citation.volume | 3248 | - |
dc.contributor.affiliatedAuthor | Lee, JH | - |
dc.identifier.scopusid | 2-s2.0-26444559017 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
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