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DC FieldValueLanguage
dc.contributorDepartment of Computingen_US
dc.creatorWong, Wing-sze-
dc.identifier.urihttps://theses.lib.polyu.edu.hk/handle/200/1832-
dc.languageEnglishen_US
dc.publisherHong Kong Polytechnic University-
dc.rightsAll rights reserveden_US
dc.titleFinding and estimating near optimal queriesen_US
dcterms.abstractThe ultimate objective of IR systems is to obtain optimal retrieval effectiveness. However, the best MAP values of the state-of-the-art IR systems are typically below 35% in the ad hoc automatic retrieval of TREC evaluations. This value is still far below the theoretical optimal retrieval effectiveness of 100%. In this study, we investigate whether it is possible to achieve near optimal retrieval effectiveness using the existing IR systems by formulating effective queries. These effective queries are called near optimal queries because they lead the IR systems to achieve near optimal retrieval effectiveness. Our near optimal queries are defined so as not to include the trivially good effective terms. We propose two strategies, the Idealized Relevance Feedback, and the Combinatorial Optimization Search, to find the near optimal queries under some idealized conditions. We have experimented with a substantial number of query-formulating methods based on the strategies and have evaluated these by using TREC test collections. The best MAP values of our near optimal queries for TREC-6, TREC-7 and TREC-8 test collections are 73%, 76% and 75%, respectively. It appears that a suitable choice of terms and a suitable choice of weights can substantially enhance the retrieval effectiveness of the existing IR systems. Based on the observations of the terms in the near optimal queries, we develop a classifier to estimate a near optimal query. The experimental results show that our classifier can improve the retrieval effectiveness of the user query in existing IR systems.en_US
dcterms.extentxii, 136 p. : ill. ; 31 cm.en_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.issued2007en_US
dcterms.educationalLevelAll Masteren_US
dcterms.educationalLevelM.Phil.en_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertations.en_US
dcterms.LCSHInformation retrieval.en_US
dcterms.LCSHInformation storage and retrieval systems.en_US
dcterms.LCSHMachine learning.en_US
dcterms.accessRightsopen accessen_US

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Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/1832