Search (4 results, page 1 of 1)

  • × theme_ss:"Automatisches Klassifizieren"
  • × theme_ss:"Suchmaschinen"
  • × type_ss:"a"
  1. Jenkins, C.: Automatic classification of Web resources using Java and Dewey Decimal Classification (1998) 0.03 
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    Abstract
    The Wolverhampton Web Library (WWLib) is a WWW search engine that provides access to UK based information. The experimental version developed in 1995, was a success but highlighted the need for a much higher degree of automation. An interesting feature of the experimental WWLib was that it organised information according to DDC. Discusses the advantages of classification and describes the automatic classifier that is being developed in Java as part of the new, fully automated WWLib
    Date
    1. 8.1996 22:08:06
    Footnote
    Contribution to a special issue devoted to the Proceedings of the 7th International World Wide Web Conference, held 14-18 April 1998, Brisbane, Australia; vgl. auch: http://www7.scu.edu.au/programme/posters/1846/com1846.htm.
    Type
    a
  2. 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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  3. Ozmutlu, S.; Cosar, G.C.: Analyzing the results of automatic new topic identification (2008) 0.00 
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    Abstract
    Purpose - Identification of topic changes within a user search session is a key issue in content analysis of search engine user queries. Recently, various studies have focused on new topic identification/session identification of search engine transaction logs, and several problems regarding the estimation of topic shifts and continuations were observed in these studies. This study aims to analyze the reasons for the problems that were encountered as a result of applying automatic new topic identification. Design/methodology/approach - Measures, such as cleaning the data of common words and analyzing the errors of automatic new topic identification, are applied to eliminate the problems in estimating topic shifts and continuations. Findings - The findings show that the resulting errors of automatic new topic identification have a pattern, and further research is required to improve the performance of automatic new topic identification. Originality/value - Improving the performance of automatic new topic identification would be valuable to search engine designers, so that they can develop new clustering and query recommendation algorithms, as well as custom-tailored graphical user interfaces for search engine users.
    Type
    a
  4. Krellenstein, M.: Document classification at Northern Light (1999) 0.00 
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    Type
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