-
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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Behnert, C.; Borst, T.: Neue Formen der Relevanz-Sortierung in bibliothekarischen Informationssystemen : das DFG-Projekt LibRank (2015)
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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
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Kanaeva, Z.: Ranking: Google und CiteSeer (2005)
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- Date
- 20. 3.2005 16:23:22
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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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-
Behnert, C.; Plassmeier, K.; Borst, T.; Lewandowski, D.: Evaluierung von Rankingverfahren für bibliothekarische Informationssysteme (2019)
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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
-
Kelledy, F.; Smeaton, A.F.: Signature files and beyond (1996)
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- Source
- Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon
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Crestani, F.; Dominich, S.; Lalmas, M.; Rijsbergen, C.J.K. van: Mathematical, logical, and formal methods in information retrieval : an introduction to the special issue (2003)
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- Date
- 22. 3.2003 19:27:36
-
Fan, W.; Fox, E.A.; Pathak, P.; Wu, H.: ¬The effects of fitness functions an genetic programming-based ranking discovery for Web search (2004)
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- Date
- 31. 5.2004 19:22:06
-
Furner, J.: ¬A unifying model of document relatedness for hybrid search engines (2003)
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- Date
- 11. 9.2004 17:32:22
-
Witschel, H.F.: Global term weights in distributed environments (2008)
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- Date
- 1. 8.2008 9:44:22
-
Campos, L.M. de; Fernández-Luna, J.M.; Huete, J.F.: Implementing relevance feedback in the Bayesian network retrieval model (2003)
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- Date
- 22. 3.2003 19:30:19
-
Ding, Y.; Chowdhury, G.; Foo, S.: Organsising keywords in a Web search environment : a methodology based on co-word analysis (2000)
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- Source
- Dynamism and stability in knowledge organization: Proceedings of the 6th International ISKO-Conference, 10-13 July 2000, Toronto, Canada. Ed.: C. Beghtol et al
-
Cecchini, R.L.; Lorenzetti, C.M.; Maguitman, A.G.; Brignole, N.B.: Multiobjective evolutionary algorithms for context-based search (2010)
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- Abstract
- Formulating high-quality queries is a key aspect of context-based search. However, determining the effectiveness of a query is challenging because multiple objectives, such as high precision and high recall, are usually involved. In this work, we study techniques that can be applied to evolve contextualized queries when the criteria for determining query quality are based on multiple objectives. We report on the results of three different strategies for evolving queries: (a) single-objective, (b) multiobjective with Pareto-based ranking, and (c) multiobjective with aggregative ranking. After a comprehensive evaluation with a large set of topics, we discuss the limitations of the single-objective approach and observe that both the Pareto-based and aggregative strategies are highly effective for evolving topical queries. In particular, our experiments lead us to conclude that the multiobjective techniques are superior to a baseline as well as to well-known and ad hoc query reformulation techniques.
