Search (360 results, page 1 of 18)

  • × theme_ss:"Retrievalalgorithmen"
  1. Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998) 0.06
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    Date
    1. 8.1996 22:08:06
    Source
    Computer networks and ISDN systems. 30(1998) nos.1/7, S.621-623
  2. Faloutsos, C.: Signature files (1992) 0.05
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    Date
    7. 5.1999 15:22:48
    Pages
    S.44-65
  3. Klas, C.-P.; Fuhr, N.; Schaefer, A.: Evaluating strategic support for information access in the DAFFODIL system (2004) 0.04
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    Date
    16.11.2008 16:22:48
    Pages
    S.476-487
  4. Shiri, A.A.; Revie, C.: Query expansion behavior within a thesaurus-enhanced search environment : a user-centered evaluation (2006) 0.04
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    Abstract
    The study reported here investigated the query expansion behavior of end-users interacting with a thesaurus-enhanced search system on the Web. Two groups, namely academic staff and postgraduate students, were recruited into this study. Data were collected from 90 searches performed by 30 users using the OVID interface to the CAB abstracts database. Data-gathering techniques included questionnaires, screen capturing software, and interviews. The results presented here relate to issues of search-topic and search-term characteristics, number and types of expanded queries, usefulness of thesaurus terms, and behavioral differences between academic staff and postgraduate students in their interaction. The key conclusions drawn were that (a) academic staff chose more narrow and synonymous terms than did postgraduate students, who generally selected broader and related terms; (b) topic complexity affected users' interaction with the thesaurus in that complex topics required more query expansion and search term selection; (c) users' prior topic-search experience appeared to have a significant effect on their selection and evaluation of thesaurus terms; (d) in 50% of the searches where additional terms were suggested from the thesaurus, users stated that they had not been aware of the terms at the beginning of the search; this observation was particularly noticeable in the case of postgraduate students.
    Date
    22. 7.2006 16:32:43
    Source
    Journal of the American Society for Information Science and Technology. 57(2006) no.4, S.462-478
  5. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.04
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    Source
    Information processing and management. 22(1986) no.6, S.465-476
  6. Salton, G.; Buckley, C.: Parallel text search methods (1988) 0.04
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    Source
    Communications of the Association for Computing Machinery. 31(1988), S.205-215
  7. 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
    Source
    Computer journal. 26(1983), S.239-246
  8. Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000) 0.03
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    Date
    25. 8.2005 17:42:22
    Source
    Library and information research news. 24(2000) no.77, S.30-34
  9. Daniowicz, C.; Baliski, J.: Document ranking based upon Markov chains (2001) 0.03
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    Source
    Information processing and management. 37(2001) no.4, S.623-637
  10. Belkin, N.J.; Cool, C.; Koenemann, J.; Ng, K.B.; Park, S.: Using relevance feedback and ranking in interactive searching (1996) 0.03
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    Pages
    S.181-209
  11. Salton, G.; Buckley, C.: Term-weighting approaches in automatic text retrieval (1988) 0.03
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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.
    Source
    Information processing and management. 24(1988) no.5, S.513-523
  12. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.03
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    Date
    14. 6.2015 22:12:44
    Pages
    S.222-238
  13. Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.03
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    Date
    14. 6.2015 22:12:56
    Pages
    S.144-162
  14. Cole, C.: Intelligent information retrieval: diagnosing information need : Part I: the theoretical framework for developing an intelligent IR tool (1998) 0.03
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    Source
    Information processing and management. 34(1998) no.6, S.709-720
  15. Cole, C.: Intelligent information retrieval: diagnosing information need : Part II: uncertainty expansion in a prototype of a diagnostic IR tool (1998) 0.03
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    Source
    Information processing and management. 34(1998) no.6, S.721-731
  16. Information retrieval : data structures and algorithms (1992) 0.02
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    Content
    An edited volume containing data structures and algorithms for information retrieval including a disk with examples written in C. for prgrammers and students interested in parsing text, automated indexing, its the first collection in book form of the basic data structures and algorithms that are critical to the storage and retrieval of documents. ------------------Enthält die Kapitel: FRAKES, W.B.: Introduction to information storage and retrieval systems; BAEZA-YATES, R.S.: Introduction to data structures and algorithms related to information retrieval; HARMAN, D. u.a.: Inverted files; FALOUTSOS, C.: Signature files; GONNET, G.H. u.a.: New indices for text: PAT trees and PAT arrays; FORD, D.A. u. S. CHRISTODOULAKIS: File organizations for optical disks; FOX, C.: Lexical analysis and stoplists; FRAKES, W.B.: Stemming algorithms; SRINIVASAN, P.: Thesaurus construction; BAEZA-YATES, R.A.: String searching algorithms; HARMAN, D.: Relevance feedback and other query modification techniques; WARTIK, S.: Boolean operators; WARTIK, S. u.a.: Hashing algorithms; HARMAN, D.: Ranking algorithms; FOX, E.: u.a.: Extended Boolean models; RASMUSSEN, E.: Clustering algorithms; HOLLAAR, L.: Special-purpose hardware for information retrieval; STANFILL, C.: Parallel information retrieval algorithms
    Footnote
    Rez. in: Computing reviews. July 1993, S.341-342 (G. Salton)
    Pages
    504 S
    Type
    s
  17. MacFarlane, A.; Robertson, S.E.; McCann, J.A.: Parallel computing for passage retrieval (2004) 0.02
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    Date
    20. 1.2007 18:30:22
    Source
    Aslib proceedings. 56(2004) no.4, S.201-211
  18. Losada, D.E.; Barreiro, A.: Emebedding term similarity and inverse document frequency into a logical model of information retrieval (2003) 0.02
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    Date
    22. 3.2003 19:27:23
    Source
    Journal of the American Society for Information Science and technology. 54(2003) no.4, S.285-301
  19. Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014) 0.02
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    Date
    22. 8.2014 17:05:18
    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.9, S.1939-1943
  20. Tober, M.; Hennig, L.; Furch, D.: SEO Ranking-Faktoren und Rang-Korrelationen 2014 : Google Deutschland (2014) 0.02
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    Date
    13. 9.2014 14:45:22
    Pages
    91 S

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