Search (105 results, page 1 of 6)

  • × theme_ss:"Retrievalalgorithmen"
  1. Ruthven, I.; Lalmas, M.: Selective relevance feedback using term characteristics (1999) 0.04
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    Source
    Vocabulary as a central concept in digital libraries: interdisciplinary concepts, challenges, and opportunities : proceedings of the Third International Conference an Conceptions of Library and Information Science (COLIS3), Dubrovnik, Croatia, 23-26 May 1999. Ed. by T. Arpanac et al
  2. Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983) 0.03
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    Date
    30. 3.2001 13:32:22
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  3. Gonnet, G.H.; Snider, T.; Baeza-Yates, R.A.: New indices for text : PAT trees and PAT arrays (1992) 0.03
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    Source
    Information retrieval: data structures and algorithms. Ed.: W.B. Frakes u. R. Baeza-Yates
  4. Zhang, W.; Yoshida, T.; Tang, X.: ¬A comparative study of TF*IDF, LSI and multi-words for text classification (2011) 0.02
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    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  5. Faloutsos, C.: Signature files (1992) 0.02
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    Date
    7. 5.1999 15:22:48
    Source
    Information retrieval: data structures and algorithms. Ed.: W.B. Frakes u. R. Baeza-Yates
  6. Klas, C.-P.; Fuhr, N.; Schaefer, A.: Evaluating strategic support for information access in the DAFFODIL system (2004) 0.02
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    Date
    16.11.2008 16:22:48
    Source
    Research and advanced technology for digital libraries : 8th European conference, ECDL 2004, Bath, UK, September 12-17, 2004 : proceedings. Eds.: Heery, R. u. E. Lyon
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  7. Fuhr, N.: Theorie des Information Retrieval I : Modelle (2004) 0.02
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    Source
    Grundlagen der praktischen Information und Dokumentation. 5., völlig neu gefaßte Ausgabe. 2 Bde. Hrsg. von R. Kuhlen, Th. Seeger u. D. Strauch. Begründet von Klaus Laisiepen, Ernst Lutterbeck, Karl-Heinrich Meyer-Uhlenried. Bd.1: Handbuch zur Einführung in die Informationswissenschaft und -praxis
  8. Symonds, M.; Bruza, P.; Zuccon, G.; Koopman, B.; Sitbon, L.; Turner, I.: Automatic query expansion : a structural linguistic perspective (2014) 0.02
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    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  9. Liddy, E.D.; Diamond, T.; McKenna, M.: DR-LINK in TIPSTER (2000) 0.02
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  10. Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998) 0.02
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    Date
    1. 8.1996 22:08:06
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  11. Niemi, T.; Junkkari, M.; Järvelin, K.; Viita, S.: Advanced query language for manipulating complex entities (2004) 0.01
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  12. Cole, C.: Intelligent information retrieval: diagnosing information need : Part I: the theoretical framework for developing an intelligent IR tool (1998) 0.01
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  13. Efthimiadis, E.N.: User choices : a new yardstick for the evaluation of ranking algorithms for interactive query expansion (1995) 0.01
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    Date
    22. 2.1996 13:14:10
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  14. Song, D.; Bruza, P.D.: Towards context sensitive information inference (2003) 0.01
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    Date
    22. 3.2003 19:35:46
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  15. Shiri, A.A.; Revie, C.: Query expansion behavior within a thesaurus-enhanced search environment : a user-centered evaluation (2006) 0.01
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    Date
    22. 7.2006 16:32:43
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  16. Wills, R.S.: Google's PageRank : the math behind the search engine (2006) 0.01
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    Abstract
    Approximately 91 million American adults use the Internet on a typical day The number-one Internet activity is reading and writing e-mail. Search engine use is next in line and continues to increase in popularity. In fact, survey findings indicate that nearly 60 million American adults use search engines on a given day. Even though there are many Internet search engines, Google, Yahoo!, and MSN receive over 81% of all search requests. Despite claims that the quality of search provided by Yahoo! and MSN now equals that of Google, Google continues to thrive as the search engine of choice, receiving over 46% of all search requests, nearly double the volume of Yahoo! and over four times that of MSN. I use Google's search engine on a daily basis and rarely request information from other search engines. One day, I decided to visit the homepages of Google. Yahoo!, and MSN to compare the quality of search results. Coffee was on my mind that day, so I entered the simple query "coffee" in the search box at each homepage. Table 1 shows the top ten (unsponsored) results returned by each search engine. Although ordered differently, two webpages, www.peets.com and www.coffeegeek.com, appear in all three top ten lists. In addition, each pairing of top ten lists has two additional results in common. Depending on the information I hoped to obtain about coffee by using the search engines, I could argue that any one of the three returned better results: however, I was not looking for a particular webpage, so all three listings of search results seemed of equal quality. Thus, I plan to continue using Google. My decision is indicative of the problem Yahoo!, MSN, and other search engine companies face in the quest to obtain a larger percentage of Internet search volume. Search engine users are loyal to one or a few search engines and are generally happy with search results. Thus, as long as Google continues to provide results deemed high in quality, Google likely will remain the top search engine. But what set Google apart from its competitors in the first place? The answer is PageRank. In this article I explain this simple mathematical algorithm that revolutionized Web search.
  17. Dang, E.K.F.; Luk, R.W.P.; Allan, J.; Ho, K.S.; Chung, K.F.L.; Lee, D.L.: ¬A new context-dependent term weight computed by boost and discount using relevance information (2010) 0.01
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    Abstract
    We studied the effectiveness of a new class of context-dependent term weights for information retrieval. Unlike the traditional term frequency-inverse document frequency (TF-IDF), the new weighting of a term t in a document d depends not only on the occurrence statistics of t alone but also on the terms found within a text window (or "document-context") centered on t. We introduce a Boost and Discount (B&D) procedure which utilizes partial relevance information to compute the context-dependent term weights of query terms according to a logistic regression model. We investigate the effectiveness of the new term weights compared with the context-independent BM25 weights in the setting of relevance feedback. We performed experiments with title queries of the TREC-6, -7, -8, and 2005 collections, comparing the residual Mean Average Precision (MAP) measures obtained using B&D term weights and those obtained by a baseline using BM25 weights. Given either 10 or 20 relevance judgments of the top retrieved documents, using the new term weights yields improvement over the baseline for all collections tested. The MAP obtained with the new weights has relative improvement over the baseline by 3.3 to 15.2%, with statistical significance at the 95% confidence level across all four collections.
  18. Robertson, S.E.: ¬The probability ranking principle in IR (1977) 0.01
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willet. San Francisco: Morgan Kaufmann 1997. S.281-286.
  19. Salton, G.; Buckley, C.: Term-weighting approaches in automatic text retrieval (1988) 0.01
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.323-328.
  20. Sparck Jones, K.: Search term relevance weighting given little relevance information (1979) 0.01
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.329-338.

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