Search (55 results, page 2 of 3)

  • × theme_ss:"Retrievalalgorithmen"
  1. Chang, R.: ¬The development of indexing technology (1993) 0.01 
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    Source
    Library software review. 12(1993) no.3, S.30-35
  2. Tenopir, C.: Online databases : natural language searching with WIN (1993) 0.01 
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    Source
    Library journal. 118(1993) no.18, S.54-56
  3. Robertson, A.M.; Willett, P.: Use of genetic algorithms in information retrieval (1995) 0.01 
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    Imprint
    London : British Library Research and Development Department
  4. Nakkouzi, Z.S.; Eastman, C.M.: Query formulation for handling negation in information retrieval systems (1990) 0.01 
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    Abstract
    Queries containing negation are widely recognised as presenting problems for both users and systems. In information retrieval systems such problems usually manifest themselves in the use of the NOT operator. Describes an algorithm to transform Boolean queries with negated terms into queries without negation; the transformation process is based on the use of a hierarchical thesaurus. Examines a set of user requests submitted to the Thomas Cooper Library at the University of South Carolina to determine the pattern and frequency of use of negation.
  5. Schamber, L.; Bateman, J.: Relevance criteria uses and importance : progress in development of a measurement scale (1999) 0.01 
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    Abstract
    The criteria employed by end-users in making relevance judgments can be powerful and useful indicators of the values users ascribe to a variety of factors in their information seeking and use situations. This paper describes intermediate results in a long-term project intended to develop a measurement scale based on users' relevance criteria. The five tests that are reported here have involved 350 users in an effort to progressively refine and validate the scale content. The range of research questions and types of users and information environments have gradually been expanded to assess the adaptability and transferability of the instrument. The instrument provides quantitative data, notably criterion importance ratings that can be analyzed using several techniques. The substantive findings confirm those of previous studies on relevance evaluation behavior
  6. MacFarlane, A.; Robertson, S.E.; McCann, J.A.: Parallel computing for passage retrieval (2004) 0.01 
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    Date
    20. 1.2007 18:30:22
  7. Faloutsos, C.: Signature files (1992) 0.01 
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    Date
    7. 5.1999 15:22:48
  8. Losada, D.E.; Barreiro, A.: Emebedding term similarity and inverse document frequency into a logical model of information retrieval (2003) 0.01 
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    Date
    22. 3.2003 19:27:23
  9. Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014) 0.01 
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    Date
    22. 8.2014 17:05:18
  10. Tober, M.; Hennig, L.; Furch, D.: SEO Ranking-Faktoren und Rang-Korrelationen 2014 : Google Deutschland (2014) 0.01 
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    Date
    13. 9.2014 14:45:22
  11. Lewandowski, D.: How can library materials be ranked in the OPAC? (2009) 0.01 
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    Abstract
    Some Online Public Access Catalogues offer a ranking component. However, ranking there is merely text-based and is doomed to fail due to limited text in bibliographic data. The main assumption for the talk is that we are in a situation where the appropriate ranking factors for OPACs should be defined, while the implementation is no major problem. We must define what we want, and not so much focus on the technical work. Some deep thinking is necessary on the "perfect results set" and how we can achieve it through ranking. The talk presents a set of potential ranking factors and clustering possibilities for further discussion. A look at commercial Web search engines could provide us with ideas how ranking can be improved with additional factors. Search engines are way beyond pure text-based ranking and apply ranking factors in the groups like popularity, freshness, personalisation, etc. The talk describes the main factors used in search engines and how derivatives of these could be used for libraries' purposes. The goal of ranking is to provide the user with the best-suitable results on top of the results list. How can this goal be achieved with the library catalogue and also concerning the library's different collections and databases? The assumption is that ranking of such materials is a complex problem and is yet nowhere near solved. Libraries should focus on ranking to improve user experience.
  12. Robertson, S.E.: OKAPI at TREC-3 (1995) 0.01 
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    Imprint
    London : British Library
  13. Lalmas, M.: XML information retrieval (2009) 0.01 
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    Source
    Encyclopedia of library and information sciences. 3rd ed. Ed.: M.J. Bates
  14. Dannenberg, R.B.; Birmingham, W.P.; Pardo, B.; Hu, N.; Meek, C.; Tzanetakis, G.: ¬A comparative evaluation of search techniques for query-by-humming using the MUSART testbed (2007) 0.01 
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    Abstract
    Query-by-humming systems offer content-based searching for melodies and require no special musical training or knowledge. Many such systems have been built, but there has not been much useful evaluation and comparison in the literature due to the lack of shared databases and queries. The MUSART project testbed allows various search algorithms to be compared using a shared framework that automatically runs experiments and summarizes results. Using this testbed, the authors compared algorithms based on string alignment, melodic contour matching, a hidden Markov model, n-grams, and CubyHum. Retrieval performance is very sensitive to distance functions and the representation of pitch and rhythm, which raises questions about some previously published conclusions. Some algorithms are particularly sensitive to the quality of queries. Our queries, which are taken from human subjects in a realistic setting, are quite difficult, especially for n-gram models. Finally, simulations on query-by-humming performance as a function of database size indicate that retrieval performance falls only slowly as the database size increases.
  15. Ravana, S.D.; Rajagopal, P.; Balakrishnan, V.: Ranking retrieval systems using pseudo relevance judgments (2015) 0.01 
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    Date
    20. 1.2015 18:30:22
    18. 9.2018 18:22:56
  16. Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998) 0.01 
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    Date
    1. 8.1996 22:08:06
  17. Kanaeva, Z.: Ranking: Google und CiteSeer (2005) 0.01 
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    20. 3.2005 16:23:22
  18. Yan, E.; Ding, Y.; Sugimoto, C.R.: P-Rank: an indicator measuring prestige in heterogeneous scholarly networks (2011) 0.01 
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    Abstract
    Ranking scientific productivity and prestige are often limited to homogeneous networks. These networks are unable to account for the multiple factors that constitute the scholarly communication and reward system. This study proposes a new informetric indicator, P-Rank, for measuring prestige in heterogeneous scholarly networks containing articles, authors, and journals. P-Rank differentiates the weight of each citation based on its citing papers, citing journals, and citing authors. Articles from 16 representative library and information science journals are selected as the dataset. Principle Component Analysis is conducted to examine the relationship between P-Rank and other bibliometric indicators. We also compare the correlation and rank variances between citation counts and P-Rank scores. This work provides a new approach to examining prestige in scholarly communication networks in a more comprehensive and nuanced way.
  19. Joss, M.W.; Wszola, S.: ¬The engines that can : text search and retrieval software, their strategies, and vendors (1996) 0.01 
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    Date
    12. 9.1996 13:56:22
  20. Kelledy, F.; Smeaton, A.F.: Signature files and beyond (1996) 0.01 
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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

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