Search (65 results, page 1 of 4)

  • × theme_ss:"Automatisches Indexieren"
  1. Wolfekuhler, M.R.; Punch, W.F.: Finding salient features for personal Web pages categories (1997) 0.02
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    Date
    1. 8.1996 22:08:06
    Source
    Computer networks and ISDN systems. 29(1997) no.8, S.1147-1156
  2. Franke-Maier, M.: Anforderungen an die Qualität der Inhaltserschließung im Spannungsfeld von intellektuell und automatisch erzeugten Metadaten (2018) 0.02
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    Abstract
    Spätestens seit dem Deutschen Bibliothekartag 2018 hat sich die Diskussion zu den automatischen Verfahren der Inhaltserschließung der Deutschen Nationalbibliothek von einer politisch geführten Diskussion in eine Qualitätsdiskussion verwandelt. Der folgende Beitrag beschäftigt sich mit Fragen der Qualität von Inhaltserschließung in digitalen Zeiten, wo heterogene Erzeugnisse unterschiedlicher Verfahren aufeinandertreffen und versucht, wichtige Anforderungen an Qualität zu definieren. Dieser Tagungsbeitrag fasst die vom Autor als Impulse vorgetragenen Ideen beim Workshop der FAG "Erschließung und Informationsvermittlung" des GBV am 29. August 2018 in Kiel zusammen. Der Workshop fand im Rahmen der 22. Verbundkonferenz des GBV statt.
  3. Kuhlen, R.: Morphologische Relationen durch Reduktionsalgorithmen (1974) 0.01
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    Date
    29. 1.2011 14:56:29
  4. Maas, H.-D.: Indexieren mit AUTINDEX (2006) 0.01
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    Abstract
    Wenn man ein Computerprogramm besitzt, das einem zu fast jedem Textwort dessen grammatische Merkmale bestimmt und außerdem noch seine interne Struktur und einige semantische Informationen liefert, dann fragt man sich irgendwann: Könnte ich nicht auf der Grundlage dieser Angaben einen Text global charakterisieren, etwa indem ich versuche, die wichtigen Wörter dieses Textes zu errechnen? Die häufigsten Textwörter können es nicht sein, denn gerade sie sind sehr nichtssagend. Die seltensten Textwörter sind zwar aussagekräftig, aber sie sind zu viele - die meisten Lemmata eines Textes erscheinen nur ein einziges Mal. Irgendwie müsste man den Wortschatz einschränken können. Die rettende Idee war: Wir tun so, als seien die semantischen Merkmale Wörter, denn dann enthält der Wortschatz dieser Sprache nur noch etwa hundert Elemente, weil unsere morphologische Analyse (Mpro) rund 100 semantische Features verwendet. Wir vermuteten nun, dass die häufig vorkommenden Features wichtig für den Text sind und die selteneren als Ausreißer betrachten werden können. Die Implementierung dieser Idee ist der Urahn unseres Programmpaketes AUTINDEX zur automatischen Indexierung von Texten. Dieses allererste Programm erstellte also zu einem Text eine Statistik der semantischen Merkmale und gab die drei häufigsten Klassen mit den zugehörigen Lemmata aus. Das Ergebnis war verblüffend: Auf den ersten Blick konnte man sehen, worum es in dem Text ging. Bei näherem Hinsehen wurden aber auch Unzulänglichkeiten deutlich. Einige der Schlagwörter waren doch ziemlich nichtssagend, andere hätte man gerne in der Liste gehabt, und schließlich hätte man sich noch eine ganz globale Charakterisierung des Textes durch die Angabe von Fachgebieten gewünscht, etwa in der Form: Der Text hat mit Politik oder Wirtschaft zu tun, er berichtet über einen Unfall, eine Feierlichkeit usw. Es wurde also sofort deutlich, dass das Programm ohne eine weitere Wissensquelle keine wirklich guten Ergebnisse würde liefern können. Man braucht also einen Thesaurus, ein Wörterbuch, in dem einzelne Lemmata und auch mehrwortige Ausdrücke mit zusätzlichen Informationen versehen sind.
  5. Panyr, J.: STEINADLER: ein Verfahren zur automatischen Deskribierung und zur automatischen thematischen Klassifikation (1978) 0.01
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    Source
    Nachrichten für Dokumentation. 29(1978), S.92-96
  6. Salton, G.; Yang, C.S.: On the specification of term values in automatic indexing (1973) 0.01
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    Source
    Journal of documentation. 29(1973), S.351-372
  7. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.01
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    Source
    Information processing and management. 22(1986) no.6, S.465-476
  8. Fuhr, N.; Niewelt, B.: ¬Ein Retrievaltest mit automatisch indexierten Dokumenten (1984) 0.01
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    Date
    20.10.2000 12:22:23
  9. Hlava, M.M.K.: Automatic indexing : comparing rule-based and statistics-based indexing systems (2005) 0.01
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    Source
    Information outlook. 9(2005) no.8, S.22-23
  10. Junger, U.: Möglichkeiten und Probleme automatischer Erschließungsverfahren in Bibliotheken : Bericht vom KASCADE-Workshop in der Universitäts- und Landesbibliothek Düsseldorf (1999) 0.01
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    Date
    23.10.1996 17:26:29
  11. Spitters, M.J.: Adjust : automatische thesauriele ontsluiting van grote hoeveelheden krantenartikelen (1999) 0.01
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    Source
    Informatie professional. 3(1999) no.10, S.29-31
  12. Knorz, G.: Automatische Indexierung (1994) 0.01
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    Date
    29. 1.2011 17:56:21
  13. Nicoletti, M.: Automatische Indexierung (2001) 0.01
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    Date
    29. 9.2017 12:00:04
  14. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.01
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    Date
    14. 6.2015 22:12:44
  15. Hauer, M.: Automatische Indexierung (2000) 0.01
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    Source
    Wissen in Aktion: Wege des Knowledge Managements. 22. Online-Tagung der DGI, Frankfurt am Main, 2.-4.5.2000. Proceedings. Hrsg.: R. Schmidt
  16. Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.01
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    Date
    14. 6.2015 22:12:56
  17. Hauer, M.: Tiefenindexierung im Bibliothekskatalog : 17 Jahre intelligentCAPTURE (2019) 0.01
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    Source
    B.I.T.online. 22(2019) H.2, S.163-166
  18. Salton, G.: Another look at automatic text-retrieval systems (1986) 0.01
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    Source
    Communications of the Association for Computing Machinery. 29(1986), S.648-656
  19. 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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    Date
    16. 8.1998 12:30:29
  20. Hüther, H.: Selix im DFG-Projekt Kascade (1998) 0.01
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    Date
    25. 8.2000 19:55:29

Years

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  • d 29
  • nl 2
  • ja 1
  • ru 1
  • More… Less…

Types

  • a 59
  • el 3
  • x 3
  • m 2
  • p 1
  • More… Less…