-
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
- Pages
- S.131-160
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Jones, K.: Linguistic searching versus relevance ranking : DR-LINK and TARGET (1999)
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- Source
- Online and CD-ROM review. 23(1999) no.2, S.67-80
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Herrera-Viedma, E.; Cordón, O.; Herrera, J.C.; Luqe, M.: ¬An IRS based on multi-granular lnguistic information (2003)
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- Abstract
- An information retrieval system (IRS) based on fuzzy multi-granular linguistic information is proposed. The system has an evaluation method to process multi-granular linguistic information, in such a way that the inputs to the IRS are represented in a different linguistic domain than the outputs. The system accepts Boolean queries whose terms are weighted by means of the ordinal linguistic values represented by the linguistic variable "Importance" assessed an a label set S. The system evaluates the weighted queries according to a threshold semantic and obtains the linguistic retrieval status values (RSV) of documents represented by a linguistic variable "Relevance" expressed in a different label set S'. The advantage of this linguistic IRS with respect to others is that the use of the multi-granular linguistic information facilitates and improves the IRS-user interaction
- Pages
- S.372-378
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Brenner, E.H.: Beyond Boolean : new approaches in information retrieval; the quest for intuitive online search systems past, present & future (1995)
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- Pages
- XV,143 S
- Type
- s
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Hoenkamp, E.; Bruza, P.D.; Song, D.; Huang, Q.: ¬An effective approach to verbose queries using a limited dependencies language model (2009)
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- Footnote
- Vgl.: http://link.springer.com/chapter/10.1007%2F978-3-642-04417-5_11. Vgl. auch: http://eprints.qut.edu.au/47258/> (Accepted Version, 5 s.) / <http://eprints.qut.edu.au/29631/> (Conference Paper, 12 s.).
- Pages
- S.116-127
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French, J.C.; Powell, A.L.; Schulman, E.: Using clustering strategies for creating authority files (2000)
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- Source
- Journal of the American Society for Information Science. 51(2000) no.8, S.774-786
-
Ponte, J.M.: Language models for relevance feedback (2000)
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- Pages
- S.73-95
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Beitzel, S.M.; Jensen, E.C.; Chowdhury, A.; Grossman, D.; Frieder, O; Goharian, N.: Fusion of effective retrieval strategies in the same information retrieval system (2004)
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- Source
- Journal of the American Society for Information Science and Technology. 55(2004) no.10, S.859-868
-
Radev, D.; Fan, W.; Qu, H.; Wu, H.; Grewal, A.: Probabilistic question answering on the Web (2005)
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- Source
- Journal of the American Society for Information Science and Technology. 56(2005) no.6, S.571-583
-
Abu-Salem, H.; Al-Omari, M.; Evens, M.W.: Stemming methodologies over individual query words for an Arabic information retrieval system (1999)
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- Source
- Journal of the American Society for Information Science. 50(1999) no.6, S.524-529
-
Symonds, M.; Bruza, P.; Zuccon, G.; Koopman, B.; Sitbon, L.; Turner, I.: Automatic query expansion : a structural linguistic perspective (2014)
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- Journal of the Association for Information Science and Technology. 65(2014) no.8, S.1577-1596
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Hoenkamp, E.; Bruza, P.: How everyday language can and will boost effective information retrieval (2015)
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- Source
- Journal of the Association for Information Science and Technology. 66(2015) no.8, S.1546-1558
