Search (4 results, page 1 of 1)

  • × theme_ss:"Retrievalstudien"
  • × theme_ss:"Semantisches Umfeld in Indexierung u. Retrieval"
  1. Robertson, S.E.; Walker, S.; Hancock-Beaulieu, M.M.: Large test collection experiments of an operational, interactive system : OKAPI at TREC (1995) 0.01 
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    Abstract
    The Okapi system has been used in a series of experiments on the TREC collections, investiganting probabilistic methods, relevance feedback, and query expansion, and interaction issues. Some new probabilistic models have been developed, resulting in simple weigthing functions that take account of document length and within document and within query term frequency. All have been shown to be beneficial when based on large quantities of relevance data as in the routing task. Interaction issues are much more difficult to evaluate in the TREC framework, and no benefits have yet been demonstrated from feedback based on small numbers of 'relevant' items identified by intermediary searchers
    Source
    Information processing and management. 31(1995) no.3, S.345-360
  2. Ekmekcioglu, F.C.; Robertson, A.M.; Willett, P.: Effectiveness of query expansion in ranked-output document retrieval systems (1992) 0.01 
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    Abstract
    Reports an evaluation of 3 methods for the expansion of natural language queries in ranked output retrieval systems. The methods are based on term co-occurrence data, on Soundex codes, and on a string similarity measure. Searches for 110 queries in a data base of 26.280 titles and abstracts suggest that there is no significant difference in retrieval effectiveness between any of these methods and unexpanded searches
  3. Kwok, K.L.: ¬A network approach to probabilistic information retrieval (1995) 0.00 
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    Abstract
    Shows how probabilistic information retrieval based on document components may be implemented as a feedforward (feedbackward) artificial neural network. The network supports adaptation of connection weights as well as the growing of new edges between queries and terms based on user relevance feedback data for training, and it reflects query modification and expansion in information retrieval. A learning rule is applied that can also be viewed as supporting sequential learning using a harmonic sequence learning rate. Experimental results with 4 standard small collections and a large Wall Street Journal collection show that small query expansion levels of about 30 terms can achieve most of the gains at the low-recall high-precision region, while larger expansion levels continue to provide gains at the high-recall low-precision region of a precision recall curve
  4. Buckley, C.; Allan, J.; Salton, G.: Automatic routing and retrieval using Smart : TREC-2 (1995) 0.00 
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    Source
    Information processing and management. 31(1995) no.3, S.315-326

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