Search (152 results, page 1 of 8)

  • × theme_ss:"Retrievalalgorithmen"
  1. Klas, C.-P.; Fuhr, N.; Schaefer, A.: Evaluating strategic support for information access in the DAFFODIL system (2004) 0.06 
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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
  2. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.05 
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    Date
    14. 6.2015 22:12:44
  3. Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.05 
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    Date
    14. 6.2015 22:12:56
  4. Fuhr, N.: Modelle im Information Retrieval (2013) 0.05 
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    Source
    Grundlagen der praktischen Information und Dokumentation. Handbuch zur Einführung in die Informationswissenschaft und -praxis. 6., völlig neu gefaßte Ausgabe. Hrsg. von R. Kuhlen, W. Semar u. D. Strauch. Begründet von Klaus Laisiepen, Ernst Lutterbeck, Karl-Heinrich Meyer-Uhlenried
  5. Schaefer, A.; Jordan, M.; Klas, C.-P.; Fuhr, N.: Active support for query formulation in virtual digital libraries : a case study with DAFFODIL (2005) 0.05 
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    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  6. Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983) 0.04 
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    Date
    30. 3.2001 13:32:22
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  7. Pfeifer, U.; Pennekamp, S.: Incremental processing of vague queries in interactive retrieval systems (1997) 0.04 
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    Source
    Hypertext - Information Retrieval - Multimedia '97: Theorien, Modelle und Implementierungen integrierter elektronischer Informationssysteme. Proceedings HIM '97. Hrsg.: N. Fuhr u.a
  8. Frakes, W.B.: Stemming algorithms (1992) 0.04 
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    Abstract
    Desribes stemming algorithms - programs that relate morphologically similar indexing and search terms. Stemming is used to improve retrieval effectiveness and to reduce the size of indexing files. Several approaches to stemming are describes - table lookup, affix removal, successor variety, and n-gram. empirical studies of stemming are summarized. The Porter stemmer is described in detail, and a full implementation in C is presented
    Source
    Information retrieval: data structures and algorithms. Ed.: W.B. Frakes u. R. Baeza-Yates
  9. Wartik, S.; Fox, E.; Heath, L.; Chen, Q.-F.: Hashing algorithms (1992) 0.04 
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    Abstract
    Discusses hashing, an information storage and retrieval technique useful for implementing many of the other structures in this book. The concepts underlying hashing are presented, along with 2 implementation strategies. The chapter also contains an extensive discussion of perfect hashing, an important optimization in information retrieval, and an O(n) algorithm to find minimal perfect hash functions for a set of keys
    Source
    Information retrieval: data structures and algorithms. Ed.: W.B. Frakes u. R. Baeza-Yates
  10. Uratani, N.; Takeda, M.: ¬A fast string-searching algorithm for multiple patterns (1993) 0.04 
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  11. Couvreur, T.R.; Benzel, R.N.; Miller, S.F.; Zeitler, D.N.; Lee, D.L.; Singhal, M.; Shivaratri, N.; Wong, W.Y.P.: ¬An analysis of performance and cost factors in searching large text databases using parallel search systems (1994) 0.03 
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  12. Losee, R.M.; Church Jr., L.: Are two document clusters better than one? : the cluster performance question for information retrieval (2005) 0.03 
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    Abstract
    When do information retrieval systems using two document clusters provide better retrieval performance than systems using no clustering? We answer this question for one set of assumptions and suggest how this may be studied with other assumptions. The "Cluster Hypothesis" asks an empirical question about the relationships between documents and user-supplied relevance judgments, while the "Cluster Performance Question" proposed here focuses an the when and why of information retrieval or digital library performance for clustered and unclustered text databases. This may be generalized to study the relative performance of m versus n clusters.
  13. Fuhr, N.: Theorie des Information Retrieval I : Modelle (2004) 0.03 
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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
  14. 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
  15. Jacucci, G.; Barral, O.; Daee, P.; Wenzel, M.; Serim, B.; Ruotsalo, T.; Pluchino, P.; Freeman, J.; Gamberini, L.; Kaski, S.; Blankertz, B.: Integrating neurophysiologic relevance feedback in intent modeling for information retrieval (2019) 0.02 
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    Abstract
    The use of implicit relevance feedback from neurophysiology could deliver effortless information retrieval. However, both computing neurophysiologic responses and retrieving documents are characterized by uncertainty because of noisy signals and incomplete or inconsistent representations of the data. We present the first-of-its-kind, fully integrated information retrieval system that makes use of online implicit relevance feedback generated from brain activity as measured through electroencephalography (EEG), and eye movements. The findings of the evaluation experiment (N = 16) show that we are able to compute online neurophysiology-based relevance feedback with performance significantly better than chance in complex data domains and realistic search tasks. We contribute by demonstrating how to integrate in interactive intent modeling this inherently noisy implicit relevance feedback combined with scarce explicit feedback. Although experimental measures of task performance did not allow us to demonstrate how the classification outcomes translated into search task performance, the experiment proved that our approach is able to generate relevance feedback from brain signals and eye movements in a realistic scenario, thus providing promising implications for future work in neuroadaptive information retrieval (IR).
  16. Hancock-Beaulieu, M.; Walker, S.: ¬An evaluation of automatic query expansion in an online library catalogue (1992) 0.02 
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    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  17. Beaulieu, M.; Jones, S.: Interactive searching and interface issues in the Okapi best match probabilistic retrieval system (1998) 0.02 
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    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  18. 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
  19. Chen, Z.; Fu, B.: On the complexity of Rocchio's similarity-based relevance feedback algorithm (2007) 0.02 
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    Abstract
    Rocchio's similarity-based relevance feedback algorithm, one of the most important query reformation methods in information retrieval, is essentially an adaptive learning algorithm from examples in searching for documents represented by a linear classifier. Despite its popularity in various applications, there is little rigorous analysis of its learning complexity in literature. In this article, the authors prove for the first time that the learning complexity of Rocchio's algorithm is O(d + d**2(log d + log n)) over the discretized vector space {0, ... , n - 1 }**d when the inner product similarity measure is used. The upper bound on the learning complexity for searching for documents represented by a monotone linear classifier (q, 0) over {0, ... , n - 1 }d can be improved to, at most, 1 + 2k (n - 1) (log d + log(n - 1)), where k is the number of nonzero components in q. Several lower bounds on the learning complexity are also obtained for Rocchio's algorithm. For example, the authors prove that Rocchio's algorithm has a lower bound Omega((d über 2)log n) on its learning complexity over the Boolean vector space {0,1}**d.
  20. Cheng, C.-S.; Chung, C.-P.; Shann, J.J.-J.: Fast query evaluation through document identifier assignment for inverted file-based information retrieval systems (2006) 0.02 
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    Abstract
    Compressing an inverted file can greatly improve query performance of an information retrieval system (IRS) by reducing disk I/Os. We observe that a good document identifier assignment (DIA) can make the document identifiers in the posting lists more clustered, and result in better compression as well as shorter query processing time. In this paper, we tackle the NP-complete problem of finding an optimal DIA to minimize the average query processing time in an IRS when the probability distribution of query terms is given. We indicate that the greedy nearest neighbor (Greedy-NN) algorithm can provide excellent performance for this problem. However, the Greedy-NN algorithm is inappropriate if used in large-scale IRSs, due to its high complexity O(N2 × n), where N denotes the number of documents and n denotes the number of distinct terms. In real-world IRSs, the distribution of query terms is skewed. Based on this fact, we propose a fast O(N × n) heuristic, called partition-based document identifier assignment (PBDIA) algorithm, which can efficiently assign consecutive document identifiers to those documents containing frequently used query terms, and improve compression efficiency of the posting lists for those terms. This can result in reduced query processing time. The experimental results show that the PBDIA algorithm can yield a competitive performance versus the Greedy-NN for the DIA problem, and that this optimization problem has significant advantages for both long queries and parallel information retrieval (IR).

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