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  • × theme_ss:"Automatisches Indexieren"
  1. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.10
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    Source
    Information processing and management. 22(1986) no.6, S.465-476
  2. Biebricher, N.; Fuhr, N.; Lustig, G.; Schwantner, M.; Knorz, G.: ¬The automatic indexing system AIR/PHYS : from research to application (1988) 0.08
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    Date
    16. 8.1998 12:51:22
    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.513-517.
    Source
    Proceedings of the 11th annual conference on research and development in information retrieval. Ed.: Y. Chiaramella
  3. Hodges, P.R.: Keyword in title indexes : effectiveness of retrieval in computer searches (1983) 0.06
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    Abstract
    A study was done to test the effectiveness of retrieval using title word searching. It was based on actual search profiles used in the Mechanized Information Center at Ohio State University, in order ro replicate as closely as possible actual searching conditions. Fewer than 50% of the relevant titles were retrieved by keywords in titles. The low rate of retrieval can be attributes to three sources: titles themselves, user and information specialist ignorance of the subject vocabulary in use, and to general language problems. Across fields it was found that the social sciences had the best retrieval rate, with science having the next best, and arts and humanities the lowest. Ways to enhance and supplement keyword in title searching on the computer and in printed indexes are discussed.
    Date
    14. 3.1996 13:22:21
  4. Bordoni, L.; Pazienza, M.T.: Documents automatic indexing in an environmental domain (1997) 0.05
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    Abstract
    Describes an application of Natural Language Processing (NLP) techniques, in HIRMA (Hypertextual Information Retrieval Managed by ARIOSTO), to the problem of document indexing by referring to a system which incorporates natural language processing techniques to determine the subject of the text of documents and to associate them with relevant semantic indexes. Describes briefly the overall system, details of its implementation on a corpus of scientific abstracts related to environmental topics and experimental evidence of the system's behaviour. Analyzes in detail an experiment designed to evaluate the system's retrieval ability in terms of recall and precision
    Source
    International forum on information and documentation. 22(1997) no.1, S.17-28
  5. Lepsky, K.; Vorhauer, J.: Lingo - ein open source System für die Automatische Indexierung deutschsprachiger Dokumente (2006) 0.05
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    Abstract
    Lingo ist ein frei verfügbares System (open source) zur automatischen Indexierung der deutschen Sprache. Bei der Entwicklung von lingo standen hohe Konfigurierbarkeit und Flexibilität des Systems für unterschiedliche Einsatzmöglichkeiten im Vordergrund. Der Beitrag zeigt den Nutzen einer linguistisch basierten automatischen Indexierung für das Information Retrieval auf. Die für eine Retrievalverbesserung zur Verfügung stehende linguistische Funktionalität von lingo wird vorgestellt und an Beispielen erläutert: Grundformerkennung, Kompositumerkennung bzw. Kompositumzerlegung, Wortrelationierung, lexikalische und algorithmische Mehrwortgruppenerkennung, OCR-Fehlerkorrektur. Der offene Systemaufbau von lingo wird beschrieben, mögliche Einsatzszenarien und Anwendungsgrenzen werden benannt.
    Date
    24. 3.2006 12:22:02
  6. Jardine, N.; Rijsbergen, C.J. van: ¬The use of hierarchic clustering in information retrieval (1971) 0.05
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    Source
    Information storage and retrieval. 7(1971), S.217-240
  7. Sparck Jones, K.; Jackson, D.M.: ¬The use of automatically obtained keyword classification for information retrieval (1970) 0.05
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    Source
    Information storage and retrieval. 5(1970), S.175-201
  8. Kantor, P.B.; Voorhees, E.: Information retrieval with scanned texts (2000) 0.05
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    Source
    Information retrieval. 2(2000), S.165-176
  9. Renz, M.: Automatische Inhaltserschließung im Zeichen von Wissensmanagement (2001) 0.05
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    Abstract
    Methoden der automatischen Inhaltserschließung werden seit mehr als 30 Jahren entwickelt, ohne in luD-Kreisen auf merkliche Akzeptanz zu stoßen. Gegenwärtig führen jedoch die steigende Informationsflut und der Bedarf an effizienten Zugriffsverfahren im Informations- und Wissensmanagement in breiten Anwenderkreisen zu einem wachsenden Interesse an diesen Methoden, zu verstärkten Anstrengungen in Forschung und Entwicklung und zu neuen Produkten. In diesem Beitrag werden verschiedene Ansätze zu intelligentem und inhaltsbasiertem Retrieval und zur automatischen Inhaltserschließung diskutiert sowie kommerziell vertriebene Softwarewerkzeuge und Lösungen präsentiert. Abschließend wird festgestellt, dass in naher Zukunft mit einer zunehmenden Automatisierung von bestimmten Komponenten des Informations- und Wissensmanagements zu rechnen ist, indem Software-Werkzeuge zur automatischen Inhaltserschließung in den Workflow integriert werden
    Date
    22. 3.2001 13:14:48
    Source
    nfd Information - Wissenschaft und Praxis. 52(2001) H.2, S.69-78
  10. RIAO 91 : Computer aided information retrieval. Conference, Barcelona, 2.-4.5.1991 (1991) 0.04
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  11. Sparck Jones, K.: Automatic keyword classification for information retrieval (1971) 0.04
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  12. Dattola, R.T.: FIRST: Flexible information retrieval system for text (1979) 0.04
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    Source
    Journal of the American Society for Information Science. 30(1979), S.9-14
  13. Garfield, E.; Sager, N.: Mechanical indexing, structural linguistics and information retrieval (1993) 0.04
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    Source
    Journal of information science. 19(1993) no.2, S.164-165
  14. Milstead, J.L.: Thesauri in a full-text world (1998) 0.04
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    Abstract
    Despite early claims to the contemporary, thesauri continue to find use as access tools for information in the full-text environment. Their mode of use is changing, but this change actually represents an expansion rather than a contrdiction of their utility. Thesauri and similar vocabulary tools can complement full-text access by aiding users in focusing their searches, by supplementing the linguistic analysis of the text search engine, and even by serving as one of the tools used by the linguistic engine for its analysis. While human indexing contunues to be used for many databases, the trend is to increase the use of machine aids for this purpose. All machine-aided indexing (MAI) systems rely on thesauri as the basis for term selection. In the 21st century, the balance of effort between human and machine will change at both input and output, but thesauri will continue to play an important role for the foreseeable future
    Date
    22. 9.1997 19:16:05
    Imprint
    Urbana-Champaign, IL : Illinois University at Urbana-Champaign, Graduate School of Library and Information Science
    Source
    Visualizing subject access for 21st century information resources: Papers presented at the 1997 Clinic on Library Applications of Data Processing, 2-4 Mar 1997, Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign. Ed.: P.A. Cochrane et al
    Theme
    Verbale Doksprachen im Online-Retrieval
  15. Salton, G.: Another look at automatic text-retrieval systems (1986) 0.03
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    Footnote
    Bezugnahme auf: Blair, D.C.: An evaluation of retrieval effectiveness for a full-text document-retrieval system. Comm. ACM 28(1985) S.280-299. - Vgl. auch: Blair, D.C.: Full text retrieval ... Int. Class. 13(1986) S.18-23; Blair, D.C., M.E. Maron: full-text information retrieval ... Inf. Proc. Man. 26(1990) S.437-447.
  16. Salton, G.; McGill, M. J.: Information Retrieval: Grundlegendes für Informationswissenschaftler (1987) 0.03
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    Content
    Enthält die Kapitel: Information Retrieval: eine Einführung; Invertierte Dateisysteme; Textanalyse und automatisches Indexieren; Die experimentellen Retrievalsysteme SMART und SIRE; Die Bewertung von Retrievalsystemen; Fortgeschrittene Retrievaltechniken; Verarbeitung natürlicher Sprache; Informationstechnologie: Hardware und Software; Datenbankmanagementsysteme; Zukünftige Entwicklungen im Information Retrieval
  17. Salton, G.: Automatic text processing : the transformation, analysis, and retrieval of information by computer (1989) 0.03
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    COMPASS
    Information retrieval / Use of / On-line computers
    Subject
    Information retrieval / Use of / On-line computers
  18. Hlava, M.M.K.: Automatic indexing : comparing rule-based and statistics-based indexing systems (2005) 0.03
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    Source
    Information outlook. 9(2005) no.8, S.22-23
  19. Plaunt, C.; Norgard, B.A.: ¬An association-based method for automatic indexing with a controlled vocabulary (1998) 0.03
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    Abstract
    In this article, we describe and test a two-stage algorithm based on a lexical collocation technique which maps from the lexical clues contained in a document representation into a controlled vocabulary list of subject headings. Using a collection of 4.626 INSPEC documents, we create a 'dictionary' of associations between the lexical items contained in the titles, authors, and abstracts, and controlled vocabulary subject headings assigned to those records by human indexers using a likelihood ratio statistic as the measure of association. In the deployment stage, we use the dictiony to predict which of the controlled vocabulary subject headings best describe new documents when they are presented to the system. Our evaluation of this algorithm, in which we compare the automatically assigned subject headings to the subject headings assigned to the test documents by human catalogers, shows that we can obtain results comparable to, and consistent with, human cataloging. In effect we have cast this as a classic partial match information retrieval problem. We consider the problem to be one of 'retrieving' (or assigning) the most probably 'relevant' (or correct) controlled vocabulary subject headings to a document based on the clues contained in that document
    Date
    11. 9.2000 19:53:22
    Source
    Journal of the American Society for Information Science. 49(1998) no.10, S.888-902
  20. Griffiths, A.; Luckhurst, H.C.; Willett, P.: Using interdocument similarity information in document retrieval systems (1986) 0.03
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    Source
    Journal of the American Society for Information Science. 37(1986) no.1, S.3-11

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