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  • × theme_ss:"Automatisches Klassifizieren"
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  1. Lindholm, J.; Schönthal, T.; Jansson , K.: Experiences of harvesting Web resources in engineering using automatic classification (2003) 0.01
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  2. Borko, H.: Research in computer based classification systems (1985) 0.01
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    Abstract
    The selection in this reader by R. M. Needham and K. Sparck Jones reports an early approach to automatic classification that was taken in England. The following selection reviews various approaches that were being pursued in the United States at about the same time. It then discusses a particular approach initiated in the early 1960s by Harold Borko, at that time Head of the Language Processing and Retrieval Research Staff at the System Development Corporation, Santa Monica, California and, since 1966, a member of the faculty at the Graduate School of Library and Information Science, University of California, Los Angeles. As was described earlier, there are two steps in automatic classification, the first being to identify pairs of terms that are similar by virtue of co-occurring as index terms in the same documents, and the second being to form equivalence classes of intersubstitutable terms. To compute similarities, Borko and his associates used a standard correlation formula; to derive classification categories, where Needham and Sparck Jones used clumping, the Borko team used the statistical technique of factor analysis. The fact that documents can be classified automatically, and in any number of ways, is worthy of passing notice. Worthy of serious attention would be a demonstra tion that a computer-based classification system was effective in the organization and retrieval of documents. One reason for the inclusion of the following selection in the reader is that it addresses the question of evaluation. To evaluate the effectiveness of their automatically derived classification, Borko and his team asked three questions. The first was Is the classification reliable? in other words, could the categories derived from one sample of texts be used to classify other texts? Reliability was assessed by a case-study comparison of the classes derived from three different samples of abstracts. The notso-surprising conclusion reached was that automatically derived classes were reliable only to the extent that the sample from which they were derived was representative of the total document collection. The second evaluation question asked whether the classification was reasonable, in the sense of adequately describing the content of the document collection. The answer was sought by comparing the automatically derived categories with categories in a related classification system that was manually constructed. Here the conclusion was that the automatic method yielded categories that fairly accurately reflected the major area of interest in the sample collection of texts; however, since there were only eleven such categories and they were quite broad, they could not be regarded as suitable for use in a university or any large general library. The third evaluation question asked whether automatic classification was accurate, in the sense of producing results similar to those obtainabie by human cIassifiers. When using human classification as a criterion, automatic classification was found to be 50 percent accurate.
    Footnote
    Original in: Classification research: Proceedings of the Second International Study Conference held at Hotel Prins Hamlet, Elsinore, Denmark, 14th-18th Sept. 1964. Ed.: Pauline Atherton. Copenhagen: Munksgaard 1965. S.220-238.
    Pages
    S.287-305
  3. Schek, M.: Automatische Klassifizierung und Visualisierung im Archiv der Süddeutschen Zeitung (2005) 0.01
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    Object
    K-Infinity
    Source
    Medienwirtschaft. 2(2005) H.1, S.20-24
  4. Reiner, U.: Automatische DDC-Klassifizierung bibliografischer Titeldatensätze der Deutschen Nationalbibliografie (2009) 0.00
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    Date
    22. 1.2010 14:41:24
  5. Schiminovich, S.: Automatic classification and retrieval of documents by means of a bibliographic pattern discovery algorithm (1971) 0.00
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    Source
    Information storage and retrieval. 6(1971), S.417-435
  6. Greiner, G.: Intellektuelles und automatisches Klassifizieren (1981) 0.00
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    Source
    VdDB/VDB-Rundschreiben. Sonderheft 71. Deutscher Bibliothekartag Regensburg, 9.-13.6.1981. S.3-7
  7. Rijsbergen, C.J. van: Automatic classification in information retrieval (1978) 0.00
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    Source
    Drexel library quarterly. 14(1978), S.75-89
  8. Panyr, J.: STEINADLER: ein Verfahren zur automatischen Deskribierung und zur automatischen thematischen Klassifikation (1978) 0.00
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    Source
    Nachrichten für Dokumentation. 29(1978), S.92-96
  9. Kwok, K.L.: ¬The use of titles and cited titles as document representations for automatic classification (1975) 0.00
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    Source
    Information processing and management. 11(1975), S.201-206
  10. Wu, M.; Fuller, M.; Wilkinson, R.: Using clustering and classification approaches in interactive retrieval (2001) 0.00
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    Source
    Information processing and management. 37(2001) no.3, S.459-484
  11. Kleinoeder, H.H.; Puzicha, J.: Automatische Katalogisierung am Beispiel einer Pilotanwendung (2002) 0.00
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    Source
    Info 7. 17(2002) H.1, S.19-21
  12. Fong, A.C.M.: Mining a Web citation database for document clustering (2002) 0.00
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    Source
    Applied artificial intelligence. 16(2002) no.4, S.283-292
  13. Major, R.L.; Ragsdale, C.T.: ¬An aggregation approach to the classification problem using multiple prediction experts (2000) 0.00
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    Source
    Information processing and management. 36(2000) no.4, S.683-696
  14. GERHARD : eine Spezialsuchmaschine für die Wissenschaft (1998) 0.00
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    Source
    RRZN-Info. 1998, BI315, S.9
  15. Ardö, A.; Koch, T.: Automatic classification applied to full-text Internet documents in a robot-generated subject index (1999) 0.00
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    Pages
    S.239-246
  16. Chan, L.M.; Lin, X.; Zeng, M.L.: Structural and multilingual approaches to subject access on the Web (2000) 0.00
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    IFLA journal. 26(2000) no.3, S.187-197
  17. Shafer, K.E.: Automatic Subject Assignment via the Scorpion System (2001) 0.00
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    Journal of library administration. 34(2001) nos.1/2, S.187-189
  18. Krellenstein, M.: Document classification at Northern Light (1999) 0.00
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    Pages
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  19. Illing, S.: Automatisiertes klinisches Codieren (2021) 0.00
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    Information - Wissenschaft und Praxis. 72(2021) H.5/6, S.285-290
  20. Mukhopadhyay, S.; Peng, S.; Raje, R.; Palakal, M.; Mostafa, J.: Multi-agent information classification using dynamic acquaintance lists (2003) 0.00
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    Source
    Journal of the American Society for Information Science and technology. 54(2003) no.10, S.966-975

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