Search (36 results, page 1 of 2)

  • × theme_ss:"Automatisches Indexieren"
  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) 0.06
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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.
  2. Wellisch, H.H.: ¬The art of indexing and some fallacies of its automation (1992) 0.02
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    Abstract
    Reviews the history of indexing, which began with the rise of the universities in the 13th century, before the invention of printing. Describes the different skills needed for indexing books, periodicals and databases. States the belief that the quest for fully automatic indexing is a futile endeavour; machine-generated indexes need the services of human post-editors if they are to be useful and acceptable
  3. Cui, H.; Boufford, D.; Selden, P.: Semantic annotation of biosystematics literature without training examples (2010) 0.01
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    Abstract
    This article presents an unsupervised algorithm for semantic annotation of morphological descriptions of whole organisms. The algorithm is able to annotate plain text descriptions with high accuracy at the clause level by exploiting the corpus itself. In other words, the algorithm does not need lexicons, syntactic parsers, training examples, or annotation templates. The evaluation on two real-life description collections in botany and paleontology shows that the algorithm has the following desirable features: (a) reduces/eliminates manual labor required to compile dictionaries and prepare source documents; (b) improves annotation coverage: the algorithm annotates what appears in documents and is not limited by predefined and often incomplete templates; (c) learns clean and reusable concepts: the algorithm learns organ names and character states that can be used to construct reusable domain lexicons, as opposed to collection-dependent patterns whose applicability is often limited to a particular collection; (d) insensitive to collection size; and (e) runs in linear time with respect to the number of clauses to be annotated.
  4. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.01
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    Source
    Information processing and management. 22(1986) no.6, S.465-476
  5. Fuhr, N.; Niewelt, B.: ¬Ein Retrievaltest mit automatisch indexierten Dokumenten (1984) 0.01
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    Date
    20.10.2000 12:22:23
  6. Hlava, M.M.K.: Automatic indexing : comparing rule-based and statistics-based indexing systems (2005) 0.01
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    Source
    Information outlook. 9(2005) no.8, S.22-23
  7. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.01
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    Date
    14. 6.2015 22:12:44
  8. Hauer, M.: Automatische Indexierung (2000) 0.01
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    Source
    Wissen in Aktion: Wege des Knowledge Managements. 22. Online-Tagung der DGI, Frankfurt am Main, 2.-4.5.2000. Proceedings. Hrsg.: R. Schmidt
  9. Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.01
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    Date
    14. 6.2015 22:12:56
  10. Hauer, M.: Tiefenindexierung im Bibliothekskatalog : 17 Jahre intelligentCAPTURE (2019) 0.01
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    Source
    B.I.T.online. 22(2019) H.2, S.163-166
  11. Biebricher, N.; Fuhr, N.; Lustig, G.; Schwantner, M.; Knorz, G.: ¬The automatic indexing system AIR/PHYS : from research to application (1988) 0.00
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    Date
    16. 8.1998 12:51:22
  12. Kutschekmanesch, S.; Lutes, B.; Moelle, K.; Thiel, U.; Tzeras, K.: Automated multilingual indexing : a synthesis of rule-based and thesaurus-based methods (1998) 0.00
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    Source
    Information und Märkte: 50. Deutscher Dokumentartag 1998, Kongreß der Deutschen Gesellschaft für Dokumentation e.V. (DGD), Rheinische Friedrich-Wilhelms-Universität Bonn, 22.-24. September 1998. Hrsg. von Marlies Ockenfeld u. Gerhard J. Mantwill
  13. Tsareva, P.V.: Algoritmy dlya raspoznavaniya pozitivnykh i negativnykh vkhozdenii deskriptorov v tekst i protsedura avtomaticheskoi klassifikatsii tekstov (1999) 0.00
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    Date
    1. 4.2002 10:22:41
  14. Stankovic, R. et al.: Indexing of textual databases based on lexical resources : a case study for Serbian (2016) 0.00
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    Date
    1. 2.2016 18:25:22
  15. Tsujii, J.-I.: Automatic acquisition of semantic collocation from corpora (1995) 0.00
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    Date
    31. 7.1996 9:22:19
  16. Riloff, E.: ¬An empirical study of automated dictionary construction for information extraction in three domains (1996) 0.00
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    Date
    6. 3.1997 16:22:15
  17. Lepsky, K.; Vorhauer, J.: Lingo - ein open source System für die Automatische Indexierung deutschsprachiger Dokumente (2006) 0.00
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    Date
    24. 3.2006 12:22:02
  18. Probst, M.; Mittelbach, J.: Maschinelle Indexierung in der Sacherschließung wissenschaftlicher Bibliotheken (2006) 0.00
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    Date
    22. 3.2008 12:35:19
  19. Glaesener, L.: Automatisches Indexieren einer informationswissenschaftlichen Datenbank mit Mehrwortgruppen (2012) 0.00
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    Date
    11. 9.2012 19:43:22
  20. Hodges, P.R.: Keyword in title indexes : effectiveness of retrieval in computer searches (1983) 0.00
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    Date
    14. 3.1996 13:22:21