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  • × theme_ss:"Automatisches Indexieren"
  1. Tsareva, P.V.: Algoritmy dlya raspoznavaniya pozitivnykh i negativnykh vkhozdenii deskriptorov v tekst i protsedura avtomaticheskoi klassifikatsii tekstov (1999) 0.02 
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    Date
    1. 4.2002 10:22:41
  2. Gräbnitz, V.: PASSAT: Programm zur automatischen Selektion von Stichwörtern aus Texten (1987) 0.02 
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    Inhaltserschließung von Massendaten zur Wirksamkeit informationslinguistischer Verfahren am Beispiel des Deutschen Patentinformationssystems. Hrsg. J. Krause
  3. Salton, G.: Future prospects for text-based information retrieval (1990) 0.02 
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    Pragmatische Aspekte beim Entwurf und Betrieb von Informationssystemen: Proc. des 1. Int. Symposiums für Informationswissenschaft, Universität Konstanz, 17.-19.10.1990. Hrsg.: J. Herget u. R. Kuhlen
  4. Salton, G.; Araya, J.: On the use of clustered file organizations in information search and retrieval (1990) 0.02 
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  5. Anderson, J.D.; Pérez-Carballo, J.: ¬The nature of indexing: how humans and machines analyze messages and texts for retrieval : Part I: Research and the nature of human indexing (2001) 0.02 
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  6. Thirion, B.; Leroy, J.P.; Baudic, F.; Douyère, M.; Piot, J.; Darmoni, S.J.: SDI selecting, decribing, and indexing : did you mean automatically? (2001) 0.02 
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  7. Rapke, K.: Automatische Indexierung von Volltexten für die Gruner+Jahr Pressedatenbank (2001) 0.02 
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    Abstract
    Retrieval Tests sind die anerkannteste Methode, um neue Verfahren der Inhaltserschließung gegenüber traditionellen Verfahren zu rechtfertigen. Im Rahmen einer Diplomarbeit wurden zwei grundsätzlich unterschiedliche Systeme der automatischen inhaltlichen Erschließung anhand der Pressedatenbank des Verlagshauses Gruner + Jahr (G+J) getestet und evaluiert. Untersucht wurde dabei natürlichsprachliches Retrieval im Vergleich zu Booleschem Retrieval. Bei den beiden Systemen handelt es sich zum einen um Autonomy von Autonomy Inc. und DocCat, das von IBM an die Datenbankstruktur der G+J Pressedatenbank angepasst wurde. Ersteres ist ein auf natürlichsprachlichem Retrieval basierendes, probabilistisches System. DocCat demgegenüber basiert auf Booleschem Retrieval und ist ein lernendes System, das auf Grund einer intellektuell erstellten Trainingsvorlage indexiert. Methodisch geht die Evaluation vom realen Anwendungskontext der Textdokumentation von G+J aus. Die Tests werden sowohl unter statistischen wie auch qualitativen Gesichtspunkten bewertet. Ein Ergebnis der Tests ist, dass DocCat einige Mängel gegenüber der intellektuellen Inhaltserschließung aufweist, die noch behoben werden müssen, während das natürlichsprachliche Retrieval von Autonomy in diesem Rahmen und für die speziellen Anforderungen der G+J Textdokumentation so nicht einsetzbar ist
  8. Maas, J.: Anforderungsanalyse für den Einsatz eines (semi)automatischen Indexierungsverfahrens in der Textdokumentation des ZDF (2002) 0.02 
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  9. Chevallet, J.-P.; Bruandet, M.F.: Impact de l'utilisation de multi terms sur la qualité des résponses dùn système de recherche d'information a indexation automatique (1999) 0.01 
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    Source
    Organisation des connaissances en vue de leur intégration dans les systèmes de représentation et de recherche d'information. Ed.: J. Maniez, et al
  10. Gaese, V.: "Automatische Klassifikation von Presseartikeln in der Gruner + Jahr Dokumentation" (2003) 0.01 
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    Abstract
    Das Klassifizieren von Texten, auch Indexieren, inhaltliches Erschließen oder verschlagworten genannt, gehört seit jeher zu den zwar notwendigen aber sehr aufwändigen Aufgaben von Archiven bzw. Dokumentationen. Ihre unterschiedlichen Zwecke bzw. Anforderungen sind sicher ein Grund dafür, dass es fast ebenso viele Erschließungsinventare, Thesauri oder Schlagwortverzeichnisse wie Dokumentationen gibt. Im folgenden werden Klassifizierung, Indexierung, Erschließung und Verschlagwortung synonym verwendet. In der G+J Dokumentation arbeiten heute ca. 20 Dokumentare an Auswahl und Erschließung von täglich etwa 1.100 Artikeln aus insgesamt ca. 210 Titeln. In der G+J Pressedatenbank sind aktuell ca. 7 Mio Artikel gespeichert, gut 2 Mio als digitaler Volltext (OCR/Satzdaten). Archiviert sind nur Artikel, für die die G+J Dokumentation die entsprechenden Rechte hat.
  11. Pritchard, J.: Information retrieval : smarter indexing (1991) 0.01 
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  12. Salton, G.; McGill, M. J.: Information Retrieval: Grundlegendes für Informationswissenschaftler (1987) 0.01 
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  13. Salton, G.; Allan, J.; Buckley, C.; Singhal, A.: Automatic analysis, theme generation, and summarization of machine readable texts (1994) 0.01 
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  14. Rapke, K.: Automatische Indexierung von Volltexten für die Gruner+Jahr Pressedatenbank (2001) 0.01 
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    Abstract
    Retrievaltests sind die anerkannteste Methode, um neue Verfahren der Inhaltserschließung gegenüber traditionellen Verfahren zu rechtfertigen. Im Rahmen einer Diplomarbeit wurden zwei grundsätzlich unterschiedliche Systeme der automatischen inhaltlichen Erschließung anhand der Pressedatenbank des Verlagshauses Gruner + Jahr (G+J) getestet und evaluiert. Untersucht wurde dabei natürlichsprachliches Retrieval im Vergleich zu Booleschem Retrieval. Bei den beiden Systemen handelt es sich zum einen um Autonomy von Autonomy Inc. und DocCat, das von IBM an die Datenbankstruktur der G+J Pressedatenbank angepasst wurde. Ersteres ist ein auf natürlichsprachlichem Retrieval basierendes, probabilistisches System. DocCat demgegenüber basiert auf Booleschem Retrieval und ist ein lernendes System, das aufgrund einer intellektuell erstellten Trainingsvorlage indexiert. Methodisch geht die Evaluation vom realen Anwendungskontext der Textdokumentation von G+J aus. Die Tests werden sowohl unter statistischen wie auch qualitativen Gesichtspunkten bewertet. Ein Ergebnis der Tests ist, dass DocCat einige Mängel gegenüber der intellektuellen Inhaltserschließung aufweist, die noch behoben werden müssen, während das natürlichsprachliche Retrieval von Autonomy in diesem Rahmen und für die speziellen Anforderungen der G+J Textdokumentation so nicht einsetzbar ist
  15. Anderson, J.D.; Pérez-Carballo, J.: ¬The nature of indexing: how humans and machines analyze messages and texts for retrieval : Part II: Machine indexing, and the allocation of human versus machine effort (2001) 0.01 
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  16. Peters, G.: Verschlagwortung und automatische Verfahren in der G+J Dokumentation (2003) 0.01 
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  17. Riloff, E.: ¬An empirical study of automated dictionary construction for information extraction in three domains (1996) 0.01 
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    Date
    6. 3.1997 16:22:15
  18. Glaesener, L.: Automatisches Indexieren einer informationswissenschaftlichen Datenbank mit Mehrwortgruppen (2012) 0.01 
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    Date
    11. 9.2012 19:43:22
  19. 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
  20. Bordoni, L.; Pazienza, M.T.: Documents automatic indexing in an environmental domain (1997) 0.01 
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    Source
    International forum on information and documentation. 22(1997) no.1, S.17-28

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