Search (6 results, page 1 of 1)

  • × theme_ss:"Automatisches Indexieren"
  • × theme_ss:"Retrievalstudien"
  1. Gödert, W.; Liebig, M.: Maschinelle Indexierung auf dem Prüfstand : Ergebnisse eines Retrievaltests zum MILOS II Projekt (1997) 0.01
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    Abstract
    The test ran between Nov 95-Aug 96 in Cologne Fachhochschule fur Bibliothekswesen (College of Librarianship).The test basis was a database of 190,000 book titles published between 1990-95. MILOS II mechanized indexing methods proved helpful in avoiding or reducing numbers of unsatisfied/no result retrieval searches. Retrieval from mechanised indexing is 3 times more successful than from title keyword data. MILOS II also used a standardized semantic vocabulary. Mechanised indexing demands high quality software and output data
  2. Fuhr, N.; Niewelt, B.: ¬Ein Retrievaltest mit automatisch indexierten Dokumenten (1984) 0.01
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    Date
    20.10.2000 12:22:23
  3. Toepfer, M.; Seifert, C.: Content-based quality estimation for automatic subject indexing of short texts under precision and recall constraints 0.01
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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.
  4. Buckley, C.; Allan, J.; Salton, G.: Automatic routing and retrieval using Smart : TREC-2 (1995) 0.01
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    Source
    Information processing and management. 31(1995) no.3, S.315-326
  5. Lochbaum, K.E.; Streeter, A.R.: Comparing and combining the effectiveness of latent semantic indexing and the ordinary vector space model for information retrieval (1989) 0.01
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    Source
    Information processing and management. 25(1989) no.6, S. 665-676
  6. Hodges, P.R.: Keyword in title indexes : effectiveness of retrieval in computer searches (1983) 0.01
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    Date
    14. 3.1996 13:22:21