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  • × theme_ss:"Automatisches Indexieren"
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  1. Lepsky, K.: Maschinelle Indexierung von Titelaufnahmen zur Verbesserung der sachlichen Erschließung in Online-Publikumskatalogen (1994) 0.02 
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    Footnote
    [Hausarbeit für den höheren Bibliotheksdienst 1993]. - Rez. in: Mitteilungsblatt VdB NW N.F. 45(1995) H.1, S.98-100 (H. Nohr); Knowledge organization 23(1996) no.1, S.50-52 (H. Schnelling)
    Pages
    V,121 S
    Series
    Kölner Arbeiten zum Bibliotheks- und Dokumentationswesen; H.18
  2. Klinger, K.-H.: Automatische Inhaltserschließung einer Volltextdatenbank : Machbarkeitsstudie am Beispiel der FAZ (1994) 0.01 
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  3. 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.
    Pages
    67 S
  4. Siegmüller, R.: Verfahren der automatischen Indexierung in bibliotheksbezogenen Anwendungen : Funktion und Qualität (2007) 0.01 
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    Pages
    106 S
  5. Glaesener, L.: Automatisches Indexieren einer informationswissenschaftlichen Datenbank mit Mehrwortgruppen (2012) 0.00 
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    Date
    11. 9.2012 19:43:22
    Pages
    III, 34, VII S
  6. Lohmann, H.: Verbesserung der Literatursuche durch Dokumentanreicherung und automatische Inhaltserschließung : Das Projekt 'KASCADE' an der Universitäts- und Landesbibliothek Düsseldorf (1999) 0.00 
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    Pages
    V,117,14 S
  7. Lorenz, S.: Konzeption und prototypische Realisierung einer begriffsbasierten Texterschließung (2006) 0.00 
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    Date
    22. 3.2015 9:17:30
    Pages
    XV, 147 S
  8. Yusuff, A.: Automatisches Indexing and Abstracting : Grundlagen und Beispiele (2002) 0.00 
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    Imprint
    Potsdam : Fachhochschule, FB A-B-D
    Pages
    53 S
  9. Weiner, U.: Vor uns die Dokumentenflut oder Automatische Indexierung als notwendige und sinnvolle Ergänzung zur intellektuellen Sacherschließung (2012) 0.00 
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  10. Halip, I.: Automatische Extrahierung von Schlagworten aus unstrukturierten Texten (2005) 0.00 
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    Abstract
    Durch die zunehmende Mediatisierung und Digitalisierung wird die moderne Gesellschaft immer mehr mit dem Thema der Informationsüberflutung konfrontiert. Erstaunlicherweise führt der Zuwachs an Informationen gleichzeitig zu einem Mangel an Wissen. Die Erklärung kann darin gefunden werden, dass ein großer Teil der existierenden Informationen nicht aufgefunden werden kann. Es handelt sich meistens um Informationen die auf semi- und nichtstrukturierte Daten beruhen. Schätzungen zufolge sind heute rund 80% der entscheidungsrelevanten Informationen in Unternehmen in unstrukturierter, d. h. meist textueller Form vorhanden. Die Unfähigkeit der Maschinen den Inhalt unstrukturierter Texte zu verstehen führt dazu, dass dokumentiertes Wissen schwer auffindbar ist und oft unentdeckt bleibt. Wegen des Informationsvolumens, das meistens zu groß ist, um gelesen, verstanden oder sogar benutzt zu werden, ergibt sich folgendes Problem, mit dem man konfrontiert wird: Informationen die nicht in Wissen umgewandelt werden können, bleiben als papiergebundene oder digitale Dokumente in Data-Repositories verschlossen. Angesichts der heute anfallenden Menge an Dokumenten erscheint eine manuelle Vergabe von Schlagworten nicht mehr realistisch. Deshalb entwickelt Wissensmanagement unterstützende Verfahren, die Informationen rechtzeitig, in der richtigen Qualität und den richtigen Personen verfügbar machen. Einige Schwerpunkte an denen zur Zeit geforscht wird, sind Modelle zur Repräsentation von Dokumenten, Methoden zur Ähnlichkeitsbestimmung von Anfragen zu Dokumenten und zur Indexierung von Dokumentenmengen, sowie die automatische Klassifikation. Vor diesem Hintergrund konzentriert sich diese Arbeit auf die unterschiedlichen Verfahren der automatischen Indexierung, hebt die algorithmischen Vor- und Nachteile hervor, mit dem Ziel die Funktionsweise im Bereich der unstrukturierten Texte zu analysieren. Hierfür erfolgt im 3. Kapitel eine genauere Untersuchung und Darstellung automatischer Indexierungsverfahren. Zuvor werden in Kapitel 2 grundlegende Begrifflichkeiten erklärt, eingeordnet und abgegrenzt. Abschließend werden anhand der theoretischen Darlegung Implementierungen der vorgestellten Verfahren kurz beschrieben. Die Ausarbeitung endet mit der Schlussfolgerung und dem Ausblick.
    Pages
    24 S
  11. Schneider, A.: Moderne Retrievalverfahren in klassischen bibliotheksbezogenen Anwendungen : Projekte und Perspektiven (2008) 0.00 
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    Pages
    64 S
  12. Bredack, J.: Terminologieextraktion von Mehrwortgruppen in kunsthistorischen Fachtexten (2013) 0.00 
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    Language
    a
    Pages
    VI, 66 S
  13. Schröther, C.: Automatische Indexierung, Kategorisierung und inhaltliche Erschließung von Textnachrichten (2003) 0.00 
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    Pages
    114 S
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