Search (33 results, page 1 of 2)

  • × theme_ss:"Retrievalalgorithmen"
  • × type_ss:"a"
  • × year_i:[2000 TO 2010}
  1. Archuby, C.G.: Interfaces se recuperacion para catalogos en linea con salidas ordenadas por probable relevancia (2000) 0.02 
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    Date
    29. 1.1996 18:23:13
    Footnote
    Übers. d. Titels: Interface for retrieval from online access catalogues with ranked results according to their relevance
    Source
    Ciencia da informacao. 29(2000) no.3, S.5-13
  2. Khoo, C.S.G.; Wan, K.-W.: ¬A simple relevancy-ranking strategy for an interface to Boolean OPACs (2004) 0.01 
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    Abstract
    A relevancy-ranking algorithm for a natural language interface to Boolean online public access catalogs (OPACs) was formulated and compared with that currently used in a knowledge-based search interface called the E-Referencer, being developed by the authors. The algorithm makes use of seven weIl-known ranking criteria: breadth of match, section weighting, proximity of query words, variant word forms (stemming), document frequency, term frequency and document length. The algorithm converts a natural language query into a series of increasingly broader Boolean search statements. In a small experiment with ten subjects in which the algorithm was simulated by hand, the algorithm obtained good results with a mean overall precision of 0.42 and mean average precision of 0.62, representing a 27 percent improvement in precision and 41 percent improvement in average precision compared to the E-Referencer. The usefulness of each step in the algorithm was analyzed and suggestions are made for improving the algorithm.
    Content
    "Most Web search engines accept natural language queries, perform some kind of fuzzy matching and produce ranked output, displaying first the documents that are most likely to be relevant. On the other hand, most library online public access catalogs (OPACs) an the Web are still Boolean retrieval systems that perform exact matching, and require users to express their search requests precisely in a Boolean search language and to refine their search statements to improve the search results. It is well-documented that users have difficulty searching Boolean OPACs effectively (e.g. Borgman, 1996; Ensor, 1992; Wallace, 1993). One approach to making OPACs easier to use is to develop a natural language search interface that acts as a middleware between the user's Web browser and the OPAC system. The search interface can accept a natural language query from the user and reformulate it as a series of Boolean search statements that are then submitted to the OPAC. The records retrieved by the OPAC are ranked by the search interface before forwarding them to the user's Web browser. The user, then, does not need to interact directly with the Boolean OPAC but with the natural language search interface or search intermediary. The search interface interacts with the OPAC system an the user's behalf. The advantage of this approach is that no modification to the OPAC or library system is required. Furthermore, the search interface can access multiple OPACs, acting as a meta search engine, and integrate search results from various OPACs before sending them to the user. The search interface needs to incorporate a method for converting the user's natural language query into a series of Boolean search statements, and for ranking the OPAC records retrieved. The purpose of this study was to develop a relevancyranking algorithm for a search interface to Boolean OPAC systems. This is part of an on-going effort to develop a knowledge-based search interface to OPACs called the E-Referencer (Khoo et al., 1998, 1999; Poo et al., 2000). E-Referencer v. 2 that has been implemented applies a repertoire of initial search strategies and reformulation strategies to retrieve records from OPACs using the Z39.50 protocol, and also assists users in mapping query keywords to the Library of Congress subject headings."
    Source
    Electronic library. 22(2004) no.2, S.112-120
  3. Notess, G.R.: Search engine relevance : the never-ending quest (2000) 0.01 
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    Source
    Online. 24(2000) no.3, S.35-40
  4. Crestani, F.: Combination of similarity measures for effective spoken document retrieval (2003) 0.01 
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    Source
    Journal of information science. 29(2003) no.2, S.87-96
  5. Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000) 0.01 
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    Date
    25. 8.2005 17:42:22
  6. Okada, M.; Ando, K.; Lee, S.S.; Hayashi, Y.; Aoe, J.I.: ¬An efficient substring search method by using delayed keyword extraction (2001) 0.01 
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    Date
    29. 3.2002 17:24:03
  7. Oberhauser, O.; Labner, J.: Relevance Ranking in Online-Katalogen : Informationsstand und Perspektiven (2003) 0.01 
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    Abstract
    Bekanntlich führen Suchmaschinen wie Google &Co. beider Auflistung der Suchergebnisse ein "Ranking" nach "Relevanz" durch, d.h. die Dokumente werden in absteigender Reihenfolge entsprechend ihrer Erfüllung von Relevanzkriterien ausgeben. In Online-Katalogen (OPACs) ist derlei noch nicht allgemein übliche Praxis, doch bietet etwa das im Österreichischen Bibliothekenverbund eingesetzte System Aleph 500 tatsächlich eine solche Ranking-Option an (die im Verbundkatalog auch implementiert ist). Bislang liegen allerdings kaum Informationen zur Funktionsweise dieses Features, insbesondere auch im Hinblick auf eine Hilfestellung für Benutzer, vor. Daher möchten wir mit diesem Beitrag versuchen, den in unserem Verbund bestehenden Informationsstand zum Thema "Relevance Ranking" zu erweitern. Sowohl die Verwendung einer Ranking-Option in OPACs generell als auch die sich unter Aleph 500 konkret bietenden Möglichkeiten sollen im folgenden näher betrachtet werden.
  8. Thompson, P.: Looking back: on relevance, probabilistic indexing and information retrieval (2008) 0.00 
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    Date
    31. 7.2008 19:58:29
  9. Vechtomova, O.; Karamuftuoglu, M.: Lexical cohesion and term proximity in document ranking (2008) 0.00 
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    Date
    1. 8.2008 12:29:05
  10. MacFarlane, A.; Robertson, S.E.; McCann, J.A.: Parallel computing for passage retrieval (2004) 0.00 
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    Date
    20. 1.2007 18:30:22
  11. Losada, D.E.; Barreiro, A.: Emebedding term similarity and inverse document frequency into a logical model of information retrieval (2003) 0.00 
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    Date
    22. 3.2003 19:27:23
  12. Otterbacher, J.; Erkan, G.; Radev, D.R.: Biased LexRank : passage retrieval using random walks with question-based priors (2009) 0.00 
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    Date
    22.11.2008 17:11:29
  13. Kanaeva, Z.: Ranking: Google und CiteSeer (2005) 0.00 
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    Date
    20. 3.2005 16:23:22
  14. French, J.C.; Powell, A.L.; Schulman, E.: Using clustering strategies for creating authority files (2000) 0.00 
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    Abstract
    As more online databases are integrated into digital libraries, the issue of quality control of the data becomes increasingly important, especially as it relates to the effective retrieval of information. Authority work, the need to discover and reconcile variant forms of strings in bibliographical entries, will become more critical in the future. Spelling variants, misspellings, and transliteration differences will all increase the difficulty of retrieving information. We investigate a number of approximate string matching techniques that have traditionally been used to help with this problem. We then introduce the notion of approximate word matching and show how it can be used to improve detection and categorization of variant forms. We demonstrate the utility of these approaches using data from the Astrophysics Data System and show how we can reduce the human effort involved in the creation of authority files
  15. Cannane, A.; Williams, H.E.: General-purpose compression for efficient retrieval (2001) 0.00 
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    Date
    29. 9.2001 13:59:55
  16. Kaszkiel, M.; Zobel, J.: Effective ranking with arbitrary passages (2001) 0.00 
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    Date
    29. 9.2001 14:00:39
  17. Bodoff, D.; Enache, D.; Kambil, A.; Simon, G.; Yukhimets, A.: ¬A unified maximum likelihood approach to document retrieval (2001) 0.00 
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    Date
    29. 9.2001 17:52:51
  18. Drucker, H.; Shahrary, B.; Gibbon, D.C.: Support vector machines : relevance feedback and information retrieval (2002) 0.00 
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    Date
    15. 8.2004 18:55:29
  19. Käki, M.: fKWIC: frequency-based Keyword-in-Context Index for filtering Web search results (2006) 0.00 
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    Abstract
    Enormous Web search engine databases combined with short search queries result in large result sets that are often difficult to access. Result ranking works fairly well, but users need help when it fails. For these situations, we propose a filtering interface that is inspired by keyword-in-context (KWIC) indices. The user interface lists the most frequent keyword contexts (fKWIC). When a context is selected, the corresponding results are displayed in the result list, allowing users to concentrate on the specific context. We compared the keyword context index user interface to the rank order result listing in an experiment with 36 participants. The results show that the proposed user interface was 29% faster in finding relevant results, and the precision of the selected results was 19% higher. In addition, participants showed positive attitudes toward the system.
  20. Kekäläinen, J.: Binary and graded relevance in IR evaluations : comparison of the effects on ranking of IR systems (2005) 0.00 
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    Date
    26.12.2007 20:29:18

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