-
Cole, C.: Intelligent information retrieval: diagnosing information need : Part II: uncertainty expansion in a prototype of a diagnostic IR tool (1998)
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Bauckhage, C.: Marginalizing over the PageRank damping factor (2014)
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Wu, H.; Salton, G.: ¬The estimation of term relevance weights using relevance feedback (1981)
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-
MacFarlane, A.; Robertson, S.E.; McCann, J.A.: Parallel computing for passage retrieval (2004)
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- Date
- 20. 1.2007 18:30:22
-
Losada, D.E.; Barreiro, A.: Emebedding term similarity and inverse document frequency into a logical model of information retrieval (2003)
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- Date
- 22. 3.2003 19:27:23
-
Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014)
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- Date
- 22. 8.2014 17:05:18
-
Tenopir, C.: Online databases : natural language searching with WIN (1993)
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-
Frakes, W.B.: Stemming algorithms (1992)
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- Abstract
- Desribes stemming algorithms - programs that relate morphologically similar indexing and search terms. Stemming is used to improve retrieval effectiveness and to reduce the size of indexing files. Several approaches to stemming are describes - table lookup, affix removal, successor variety, and n-gram. empirical studies of stemming are summarized. The Porter stemmer is described in detail, and a full implementation in C is presented
-
Stanfill, C.: Parallel information retrieval algorithms (1992)
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-
Hüther, H.: Selix im DFG-Projekt Kascade (1998)
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- Source
- Knowledge Management und Kommunikationssysteme: Proceedings des 6. Internationalen Symposiums für Informationswissenschaft (ISI '98) Prag, 3.-7. November 1998 / Hochschulverband für Informationswissenschaft (HI) e.V. Konstanz ; Fachrichtung Informationswissenschaft der Universität des Saarlandes, Saarbrücken. Hrsg.: Harald H. Zimmermann u. Volker Schramm
-
Dreßler, H.: Fuzzy Information Retrieval (2008)
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- Source
- Information - Wissenschaft und Praxis. 59(2008) H.6/7, S.351-352
-
Ravana, S.D.; Rajagopal, P.; Balakrishnan, V.: Ranking retrieval systems using pseudo relevance judgments (2015)
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- Date
- 20. 1.2015 18:30:22
18. 9.2018 18:22:56
-
Cheng, C.-S.; Chung, C.-P.; Shann, J.J.-J.: Fast query evaluation through document identifier assignment for inverted file-based information retrieval systems (2006)
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-
Carpineto, C.; Romano, G.: Information retrieval through hybrid navigation of lattice representations (1996)
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-
Boughanem, M.; Chrisment, C.; Tamine, L.: On using genetic algorithms for multimodal relevance optimization in information retrieval (2002)
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-
Lee, C.; Lee, G.G.: Probabilistic information retrieval model for a dependence structured indexing system (2005)
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-
López-Pujalte, C.; Guerrero-Bote, V.P.; Moya-Anegón, F. de: Genetic algorithms in relevance feedback : a second test and new contributions (2003)
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-
Jacso, P.: Testing the calculation of a realistic h-index in Google Scholar, Scopus, and Web of Science for F. W. Lancaster (2008)
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- Abstract
- This paper focuses on the practical limitations in the content and software of the databases that are used to calculate the h-index for assessing the publishing productivity and impact of researchers. To celebrate F. W. Lancaster's biological age of seventy-five, and "scientific age" of forty-five, this paper discusses the related features of Google Scholar, Scopus, and Web of Science (WoS), and demonstrates in the latter how a much more realistic and fair h-index can be computed for F. W. Lancaster than the one produced automatically. Browsing and searching the cited reference index of the 1945-2007 edition of WoS, which in my estimate has over a hundred million "orphan references" that have no counterpart master records to be attached to, and "stray references" that cite papers which do have master records but cannot be identified by the matching algorithm because of errors of omission and commission in the references of the citing works, can bring up hundreds of additional cited references given to works of an accomplished author but are ignored in the automatic process of calculating the h-index. The partially manual process doubled the h-index value for F. W. Lancaster from 13 to 26, which is a much more realistic value for an information scientist and professor of his stature.
- Object
- h-index
-
White, H. D.: Co-cited author retrieval and relevance theory : examples from the humanities (2015)
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-
Joss, M.W.; Wszola, S.: ¬The engines that can : text search and retrieval software, their strategies, and vendors (1996)
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- Date
- 12. 9.1996 13:56:22
