Search (94 results, page 4 of 5)

  • × theme_ss:"Automatisches Indexieren"
  1. Leung, C.-H.; Kan, W.-K.: ¬A statistical learning approach to automatic indexing of controlled index terms (1997) 0.01 
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  2. Lepsky, K.: Im Heuhaufen suchen - und finden : Automatische Erschließung von Internetquellen: Möglichkeiten und Grenzen (1998) 0.01 
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  3. Rapke, K.: Automatische Indexierung von Volltexten für die Gruner+Jahr Pressedatenbank (2001) 0.01 
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  4. Banerjee, K.; Johnson, M.: Improving access to archival collections with automated entity extraction (2015) 0.01 
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  5. Munkelt, J.; Schaer, P.; Lepsky, K.: Towards an IR test collection for the German National Library (2018) 0.01 
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  6. Matthews, P.; Glitre, K.: Genre analysis of movies using a topic model of plot summaries (2021) 0.01 
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  7. Ward, M.L.: ¬The future of the human indexer (1996) 0.01 
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    Date
    9. 2.1997 18:44:22
  8. Lorenz, S.: Konzeption und prototypische Realisierung einer begriffsbasierten Texterschließung (2006) 0.01 
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    Date
    22. 3.2015 9:17:30
  9. Busch, D.: Domänenspezifische hybride automatische Indexierung von bibliographischen Metadaten (2019) 0.01 
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    Source
    B.I.T.online. 22(2019) H.6, S.465-469
  10. Nohr, H.: Automatische Indexierung : Einführung in betriebliche Verfahren, Systeme und Anwendungen (2001) 0.01 
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    Classification
    BCAO (FH K)
    GHBS
    BCAO (FH K)
  11. Larroche-Boutet, V.; Pöhl, K.: ¬Das Nominalsyntagna : über die Nutzbarmachung eines logico-semantischen Konzeptes für dokumentarische Fragestellungen (1993) 0.01 
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  12. Rapke, K.: Automatische Indexierung von Volltexten für die Gruner+Jahr Pressedatenbank (2001) 0.01 
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  13. Li, W.; Wong, K.-F.; Yuan, C.: Toward automatic Chinese temporal information extraction (2001) 0.01 
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  14. Rädler, K.: In Bibliothekskatalogen "googlen" : Integration von Inhaltsverzeichnissen, Volltexten und WEB-Ressourcen in Bibliothekskataloge (2004) 0.01 
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  15. Golub, K.; Lykke, M.; Tudhope, D.: Enhancing social tagging with automated keywords from the Dewey Decimal Classification (2014) 0.01 
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  16. Tsai, C.-F.; McGarry, K.; Tait, J.: Qualitative evaluation of automatic assignment of keywords to images (2006) 0.01 
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  17. Donahue, J.; Hendricks, L.A.; Guadarrama, S.; Rohrbach, M.; Venugopalan, S.; Saenko, K.; Darrell, T.: Long-term recurrent convolutional networks for visual recognition and description (2014) 0.01 
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  18. Golub, K.; Soergel, D.; Buchanan, G.; Tudhope, D.; Lykke, M.; Hiom, D.: ¬A framework for evaluating automatic indexing or classification in the context of retrieval (2016) 0.01 
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  19. Grün, S.: Mehrwortbegriffe und Latent Semantic Analysis : Bewertung automatisch extrahierter Mehrwortgruppen mit LSA (2017) 0.01 
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    Abstract
    Die vorliegende Studie untersucht das Potenzial von Mehrwortbegriffen für das Information Retrieval. Zielsetzung der Arbeit ist es, intellektuell positiv bewertete Kandidaten mithilfe des Latent Semantic Analysis (LSA) Verfahren höher zu gewichten, als negativ bewertete Kandidaten. Die positiven Kandidaten sollen demnach bei einem Ranking im Information Retrieval bevorzugt werden. Als Kollektion wurde eine Version der sozialwissenschaftlichen GIRT-Datenbank (German Indexing and Retrieval Testdatabase) eingesetzt. Um Kandidaten für Mehrwortbegriffe zu identifizieren wurde die automatische Indexierung Lingo verwendet. Die notwendigen Kernfunktionalitäten waren Lemmatisierung, Identifizierung von Komposita, algorithmische Mehrworterkennung sowie Gewichtung von Indextermen durch das LSA-Modell. Die durch Lingo erkannten und LSAgewichteten Mehrwortkandidaten wurden evaluiert. Zuerst wurde dazu eine intellektuelle Auswahl von positiven und negativen Mehrwortkandidaten vorgenommen. Im zweiten Schritt der Evaluierung erfolgte die Berechnung der Ausbeute, um den Anteil der positiven Mehrwortkandidaten zu erhalten. Im letzten Schritt der Evaluierung wurde auf der Basis der R-Precision berechnet, wie viele positiv bewerteten Mehrwortkandidaten es an der Stelle k des Rankings geschafft haben. Die Ausbeute der positiven Mehrwortkandidaten lag bei durchschnittlich ca. 39%, während die R-Precision einen Durchschnittswert von 54% erzielte. Das LSA-Modell erzielt ein ambivalentes Ergebnis mit positiver Tendenz.
  20. Lu, K.; Mao, J.: ¬An automatic approach to weighted subject indexing : an empirical study in the biomedical domain (2015) 0.01 
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