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  • × theme_ss:"Retrievalstudien"
  1. Larsen, B.; Ingwersen, P.; Lund, B.: Data fusion according to the principle of polyrepresentation (2009) 0.00 
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    Abstract
    We report data fusion experiments carried out on the four best-performing retrieval models from TREC 5. Three were conceptually/algorithmically very different from one another; one was algorithmically similar to one of the former. The objective of the test was to observe the performance of the 11 logical data fusion combinations compared to the performance of the four individual models and their intermediate fusions when following the principle of polyrepresentation. This principle is based on cognitive IR perspective (Ingwersen & Järvelin, 2005) and implies that each retrieval model is regarded as a representation of a unique interpretation of information retrieval (IR). It predicts that only fusions of very different, but equally good, IR models may outperform each constituent as well as their intermediate fusions. Two kinds of experiments were carried out. One tested restricted fusions, which entails that only the inner disjoint overlap documents between fused models are ranked. The second set of experiments was based on traditional data fusion methods. The experiments involved the 30 TREC 5 topics that contain more than 44 relevant documents. In all tests, the Borda and CombSUM scoring methods were used. Performance was measured by precision and recall, with document cutoff values (DCVs) at 100 and 15 documents, respectively. Results show that restricted fusions made of two, three, or four cognitively/algorithmically very different retrieval models perform significantly better than do the individual models at DCV100. At DCV15, however, the results of polyrepresentative fusion were less predictable. The traditional fusion method based on polyrepresentation principles demonstrates a clear picture of performance at both DCV levels and verifies the polyrepresentation predictions for data fusion in IR. Data fusion improves retrieval performance over their constituent IR models only if the models all are quite conceptually/algorithmically dissimilar and equally and well performing, in that order of importance.
    Date
    22. 3.2009 18:48:28
  2. Rijsbergen, C.J. van: ¬A test for the separation of relevant and non-relevant documents in experimental retrieval collections (1973) 0.00 
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    Date
    19. 3.1996 11:22:12
    Source
    Journal of documentation. 29(1973) no.3, S.251-257
  3. Keyes, J.G.: Using conceptual categories of questions to measure differences in retrieval performance (1996) 0.00 
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    Abstract
    The form of a question denotes the relationship between the current state of knowledge of the questioner and the propositional content of the question. To assess whether these semantic differences have implications for information retrieval, uses CF database, a 1239 document test database containing titles and abstracts of documents pertaining to cystic fibrosis. The database has an accompanying list of 100 questions which were divided into 5 conceptual categories of questions based on their semantic representation. 2 retrieval methods were used to investigate potential diferences in outcomes across conceptual categories: the cosine measurement and the similarity measurement. The ranked results produced by different algorithms will vary for individual conceptual categories as well as for overall performance
  4. Serrano Cobos, J.; Quintero Orta, A.: Design, development and management of an information recovery system for an Internet Website : from documentary theory to practice (2003) 0.00 
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    Source
    Challenges in knowledge representation and organization for the 21st century: Integration of knowledge across boundaries. Proceedings of the 7th ISKO International Conference Granada, Spain, July 10-13, 2002. Ed.: M. López-Huertas
  5. Colace, F.; Santo, M. de; Greco, L.; Napoletano, P.: Improving relevance feedback-based query expansion by the use of a weighted word pairs approach (2015) 0.00 
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    Abstract
    In this article, the use of a new term extraction method for query expansion (QE) in text retrieval is investigated. The new method expands the initial query with a structured representation made of weighted word pairs (WWP) extracted from a set of training documents (relevance feedback). Standard text retrieval systems can handle a WWP structure through custom Boolean weighted models. We experimented with both the explicit and pseudorelevance feedback schemas and compared the proposed term extraction method with others in the literature, such as KLD and RM3. Evaluations have been conducted on a number of test collections (Text REtrivel Conference [TREC]-6, -7, -8, -9, and -10). Results demonstrated that the QE method based on this new structure outperforms the baseline.
  6. Boros, E.; Kantor, P.B.; Neu, D.J.: Pheromonic representation of user quests by digital structures (1999) 0.00 
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  7. Toepfer, M.; Seifert, C.: Content-based quality estimation for automatic subject indexing of short texts under precision and recall constraints 0.00 
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    Abstract
    Semantic annotations have to satisfy quality constraints to be useful for digital libraries, which is particularly challenging on large and diverse datasets. Confidence scores of multi-label classification methods typically refer only to the relevance of particular subjects, disregarding indicators of insufficient content representation at the document-level. Therefore, we propose a novel approach that detects documents rather than concepts where quality criteria are met. Our approach uses a deep, multi-layered regression architecture, which comprises a variety of content-based indicators. We evaluated multiple configurations using text collections from law and economics, where the available content is restricted to very short texts. Notably, we demonstrate that the proposed quality estimation technique can determine subsets of the previously unseen data where considerable gains in document-level recall can be achieved, while upholding precision at the same time. Hence, the approach effectively performs a filtering that ensures high data quality standards in operative information retrieval systems.
  8. Chu, H.: Factors affecting relevance judgment : a report from TREC Legal track (2011) 0.00 
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    Date
    12. 7.2011 18:29:22
  9. Hofstede, M.: Literatuur over onderwerpen zoeken in de OPC (1994) 0.00 
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    Source
    CRI bulletin. 29(1994), Sept., S.14-15
  10. Hancock-Beaulieu, M.; McKenzie, L.; Irving, A.: Evaluative protocols for searching behaviour in online library catalogues (1991) 0.00 
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    Date
    23. 1.1999 19:52:29
  11. Harman, D.K.: ¬The TREC test collections (2005) 0.00 
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    Date
    29. 3.1996 18:16:49
  12. Buckley, C.; Voorhees, E.M.: Retrieval system evaluation (2005) 0.00 
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    Date
    29. 3.1996 18:16:49
  13. Voiskunskii, V.G.: Evaluation of search results (2000) 0.00 
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    Source
    Encyclopedia of library and information science. Vol.66, [=Suppl.29]
  14. Harman, D.K.: ¬The TREC ad hoc experiments (2005) 0.00 
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    Date
    29. 3.1996 18:16:49
  15. Robertson, S.; Callan, J.: Routing and filtering (2005) 0.00 
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    Date
    29. 3.1996 18:16:49
  16. Beaulieu, M.: Approaches to user-based studies in information seeking and retrieval : a Sheffield perspective (2003) 0.00 
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    Source
    Journal of information science. 29(2003) no.4, S.239-248
  17. Harman, D.K.: Beyond English (2005) 0.00 
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    Date
    29. 3.1996 18:16:49
  18. Voorhees, E.M.; Garofolo, J.S.: Retrieving noisy text (2005) 0.00 
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    Date
    29. 3.1996 18:16:49
  19. Fuhr, N.; Niewelt, B.: ¬Ein Retrievaltest mit automatisch indexierten Dokumenten (1984) 0.00 
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    Date
    20.10.2000 12:22:23
  20. Tomaiuolo, N.G.; Parker, J.: Maximizing relevant retrieval : keyword and natural language searching (1998) 0.00 
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    Source
    Online. 22(1998) no.6, S.57-58

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