-
Rijsbergen, C.J. van: ¬A test for the separation of relevant and non-relevant documents in experimental retrieval collections (1973)
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- Date
- 19. 3.1996 11:22:12
-
Rijsbergen, C.J. van: Foundations of evaluation (1974)
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-
Crestani, F.; Rijsbergen, C.J. van: Information retrieval by imaging (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
-
Rijsbergen, C.J. van: Retrieval effectiveness (1981)
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-
Crestani, F.; Ruthven, I.; Sanderson, M.; Rijsbergen, C.J. van: ¬The troubles with using a logical model of IR on a large collection of documents : experimenting retrieval by logical imaging on TREC (1996)
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-
Van der Walt, H.E.A.; Brakel, P.A. van: Method for the evaluation of the retrieval effectiveness of a CD-ROM bibliographic database (1991)
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-
Information retrieval experiment (1981)
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- Content
- Enthält die Beiträge: ROBERTSON, S.E.: The methodology of information retrieval experiment; RIJSBERGEN, C.J. van: Retrieval effectiveness; BELKIN, N.: Ineffable concepts in information retrieval; TAGUE, J.M.: The pragmatics of information retrieval experimentation; LANCASTER, F.W.: Evaluation within the environment of an operating information service; BARRACLOUGH, E.D.: Opportunities for testing with online systems; KEEN, M.E.: Laboratory tests of manual systems; ODDY, R.N.: Laboratory tests: automatic systems; HEINE, M.D.: Simulation, and simulation experiments; COOPER, W.S.: Gedanken experimentation: an alternative to traditional system testing?; SPARCK JONES, K.: Actual tests - retrieval system tests; EVANS, L.: An experiment: search strategy variation in SDI profiles; SALTON, G.: The Smart environment for retrieval system evaluation - advantage and problem areas
-
Sparck Jones, K.; Rijsbergen, C.J. van: Progress in documentation : Information retrieval test collection (1976)
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-
Abdou, S.; Savoy, J.: Searching in Medline : query expansion and manual indexing evaluation (2008)
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- Abstract
- Based on a relatively large subset representing one third of the Medline collection, this paper evaluates ten different IR models, including recent developments in both probabilistic and language models. We show that the best performing IR models is a probabilistic model developed within the Divergence from Randomness framework [Amati, G., & van Rijsbergen, C.J. (2002) Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM-Transactions on Information Systems 20(4), 357-389], which result in 170% enhancements in mean average precision when compared to the classical tf idf vector-space model. This paper also reports on our impact evaluations on the retrieval effectiveness of manually assigned descriptors (MeSH or Medical Subject Headings), showing that by including these terms retrieval performance can improve from 2.4% to 13.5%, depending on the underling IR model. Finally, we design a new general blind-query expansion approach showing improved retrieval performances compared to those obtained using the Rocchio approach.
-
Ruthven, I.; Lalmas, M.; Rijsbergen, K. van: Combining and selecting characteristics of information use (2002)
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- Abstract
- Ruthven, Lalmas, and van Rijsbergen use traditional term importance measures like inverse document frequency, noise, based upon in-document frequency, and term frequency supplemented by theme value which is calculated from differences of expected positions of words in a text from their actual positions, on the assumption that even distribution indicates term association with a main topic, and context, which is based on a query term's distance from the nearest other query term relative to the average expected distribution of all query terms in the document. They then define document characteristics like specificity, the sum of all idf values in a document over the total terms in the document, or document complexity, measured by the documents average idf value; and information to noise ratio, info-noise, tokens after stopping and stemming over tokens before these processes, measuring the ratio of useful and non-useful information in a document. Retrieval tests are then carried out using each characteristic, combinations of the characteristics, and relevance feedback to determine the correct combination of characteristics. A file ranks independently of query terms by both specificity and info-noise, but if presence of a query term is required unique rankings are generated. Tested on five standard collections the traditional characteristics out preformed the new characteristics, which did, however, out preform random retrieval. All possible combinations of characteristics were also tested both with and without a set of scaling weights applied. All characteristics can benefit by combination with another characteristic or set of characteristics and performance as a single characteristic is a good indicator of performance in combination. Larger combinations tended to be more effective than smaller ones and weighting increased precision measures of middle ranking combinations but decreased the ranking of poorer combinations. The best combinations vary for each collection, and in some collections with the addition of weighting. Finally, with all documents ranked by the all characteristics combination, they take the top 30 documents and calculate the characteristic scores for each term in both the relevant and the non-relevant sets. Then taking for each query term the characteristics whose average was higher for relevant than non-relevant documents the documents are re-ranked. The relevance feedback method of selecting characteristics can select a good set of characteristics for query terms.
-
Fuhr, N.; Niewelt, B.: ¬Ein Retrievaltest mit automatisch indexierten Dokumenten (1984)
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- Date
- 20.10.2000 12:22:23
-
Tomaiuolo, N.G.; Parker, J.: Maximizing relevant retrieval : keyword and natural language searching (1998)
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- Source
- Online. 22(1998) no.6, S.57-58
-
Voorhees, E.M.; Harman, D.: Overview of the Sixth Text REtrieval Conference (TREC-6) (2000)
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- Date
- 11. 8.2001 16:22:19
-
Dalrymple, P.W.: Retrieval by reformulation in two library catalogs : toward a cognitive model of searching behavior (1990)
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- Date
- 22. 7.2006 18:43:54
-
Vegt, A. van der; Zuccon, G.; Koopman, B.: Do better search engines really equate to better clinical decisions? : If not, why not? (2021)
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-
Allan, J.; Callan, J.P.; Croft, W.B.; Ballesteros, L.; Broglio, J.; Xu, J.; Shu, H.: INQUERY at TREC-5 (1997)
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- Date
- 27. 2.1999 20:55:22
-
Ng, K.B.; Loewenstern, D.; Basu, C.; Hirsh, H.; Kantor, P.B.: Data fusion of machine-learning methods for the TREC5 routing tak (and other work) (1997)
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- Date
- 27. 2.1999 20:59:22
-
Saracevic, T.: On a method for studying the structure and nature of requests in information retrieval (1983)
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- Pages
- S.22-25
-
Sanderson, M.: ¬The Reuters test collection (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
-
Lespinasse, K.: TREC: une conference pour l'evaluation des systemes de recherche d'information (1997)
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- Date
- 1. 8.1996 22:01:00
