Search (44 results, page 1 of 3)

Berry, M.W.; Browne, M.: Understanding search engines : mathematical modeling and text retrieval (1999) 0.17

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Classification: TK5105.884.B47 1999
LCC: TK5105.884.B47 1999
Year: 1999

Ciocca, G.; Schettini, R.: ¬A relevance feedback mechanism for content-based image retrieval (1999) 0.05

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Source: Information processing and management. 35(1999) no.5, S.605-632
Year: 1999

Finding anything in the billion page Web : are algorithms the key? (1999) 0.05

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Source: Computer networks. 31(1999) no.11-16, S.1760-1761
Year: 1999

Jones, K.: Linguistic searching versus relevance ranking : DR-LINK and TARGET (1999) 0.05

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Source: Online and CD-ROM review. 23(1999) no.2, S.67-80
Year: 1999

Berry, M.W.; Browne, M.: Understanding search engines : mathematical modeling and text retrieval (2005) 0.04

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Classification: TK5105.884.B47 2005
LCC: TK5105.884.B47 2005

Courtois, M.P.; Berry, M.W.: Results ranking in Web search engines (1999) 0.04

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Source: Online. 23(1999) no.3, S.39-46
Year: 1999

Ruthven, I.; Lalmas, M.: Selective relevance feedback using term characteristics (1999) 0.04

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Source: Vocabulary as a central concept in digital libraries: interdisciplinary concepts, challenges, and opportunities : proceedings of the Third International Conference an Conceptions of Library and Information Science (COLIS3), Dubrovnik, Croatia, 23-26 May 1999. Ed. by T. Arpanac et al
Year: 1999

Chakrabarti, S.; Dom, B.; Kumar, S.R.; Raghavan, P.; Rajagopalan, S.; Tomkins, A.; Kleinberg, J.M.; Gibson, D.: Neue Pfade durch den Internet-Dschungel : Die zweite Generation von Web-Suchmaschinen (1999) 0.03

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Source: Spektrum der Wissenschaft. 1999, H.8, S.44-49
Year: 1999

Khoo, C.S.G.; Wan, K.-W.: ¬A simple relevancy-ranking strategy for an interface to Boolean OPACs (2004) 0.03
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Content

"Most Web search engines accept natural language queries, perform some kind of fuzzy matching and produce ranked output, displaying first the documents that are most likely to be relevant. On the other hand, most library online public access catalogs (OPACs) an the Web are still Boolean retrieval systems that perform exact matching, and require users to express their search requests precisely in a Boolean search language and to refine their search statements to improve the search results. It is well-documented that users have difficulty searching Boolean OPACs effectively (e.g. Borgman, 1996; Ensor, 1992; Wallace, 1993). One approach to making OPACs easier to use is to develop a natural language search interface that acts as a middleware between the user's Web browser and the OPAC system. The search interface can accept a natural language query from the user and reformulate it as a series of Boolean search statements that are then submitted to the OPAC. The records retrieved by the OPAC are ranked by the search interface before forwarding them to the user's Web browser. The user, then, does not need to interact directly with the Boolean OPAC but with the natural language search interface or search intermediary. The search interface interacts with the OPAC system an the user's behalf. The advantage of this approach is that no modification to the OPAC or library system is required. Furthermore, the search interface can access multiple OPACs, acting as a meta search engine, and integrate search results from various OPACs before sending them to the user. The search interface needs to incorporate a method for converting the user's natural language query into a series of Boolean search statements, and for ranking the OPAC records retrieved. The purpose of this study was to develop a relevancyranking algorithm for a search interface to Boolean OPAC systems. This is part of an on-going effort to develop a knowledge-based search interface to OPACs called the E-Referencer (Khoo et al., 1998, 1999; Poo et al., 2000). E-Referencer v. 2 that has been implemented applies a repertoire of initial search strategies and reformulation strategies to retrieve records from OPACs using the Z39.50 protocol, and also assists users in mapping query keywords to the Library of Congress subject headings."

Source

Electronic library. 22(2004) no.2, S.112-120

Davis, C.H.; McKim, G.W.: Systematic weighting and ranking : cutting the Gordian knot (1999) 0.02

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Source: Journal of the American Society for Information Science. 50(1999) no.7, S.626-628
Year: 1999

Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.02

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Source: Information processing and management. 22(1986) no.6, S.465-476

Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983) 0.02

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Date: 30. 3.2001 13:32:22

Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000) 0.02

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Date: 25. 8.2005 17:42:22

Martin-Bautista, M.J.; Vila, M.-A.; Larsen, H.L.: ¬A fuzzy genetic algorithm approach to an adaptive information retrieval agent (1999) 0.02

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Source: Journal of the American Society for Information Science. 50(1999) no.9, S.760-771
Year: 1999

Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.02

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Date: 14. 6.2015 22:12:44

Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.02

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Date: 14. 6.2015 22:12:56

Abu-Salem, H.; Al-Omari, M.; Evens, M.W.: Stemming methodologies over individual query words for an Arabic information retrieval system (1999) 0.02

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Source: Journal of the American Society for Information Science. 50(1999) no.6, S.524-529
Year: 1999

Schamber, L.; Bateman, J.: Relevance criteria uses and importance : progress in development of a measurement scale (1999) 0.02

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Year: 1999

Kantor, P.; Kim, M.H.; Ibraev, U.; Atasoy, K.: Estimating the number of relevant documents in enormous collections (1999) 0.01

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Year: 1999

MacFarlane, A.; Robertson, S.E.; McCann, J.A.: Parallel computing for passage retrieval (2004) 0.01

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Date: 20. 1.2007 18:30:22

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