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  • × language_ss:"e"
  • × theme_ss:"Inhaltsanalyse"
  • × year_i:[2010 TO 2020}
  1. Short, M.: Text mining and subject analysis for fiction; or, using machine learning and information extraction to assign subject headings to dime novels (2019) 0.05 
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    Abstract
    This article describes multiple experiments in text mining at Northern Illinois University that were undertaken to improve the efficiency and accuracy of cataloging. It focuses narrowly on subject analysis of dime novels, a format of inexpensive fiction that was popular in the United States between 1860 and 1915. NIU holds more than 55,000 dime novels in its collections, which it is in the process of comprehensively digitizing. Classification, keyword extraction, named-entity recognition, clustering, and topic modeling are discussed as means of assigning subject headings to improve their discoverability by researchers and to increase the productivity of digitization workflows.
    Theme
    Data Mining
  2. Raieli, R.: ¬The semantic hole : enthusiasm and caution around multimedia information retrieval (2012) 0.01 
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    Date
    22. 1.2012 13:02:10
    Source
    Knowledge organization. 39(2012) no.1, S.13-22
  3. Chen, S.-J.; Lee, H.-L.: Art images and mental associations : a preliminary exploration (2014) 0.01 
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    Source
    Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

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