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  • × theme_ss:"Automatisches Indexieren"
  1. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.03 
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    Information processing and management. 22(1986) no.6, S.465-476
  2. Fuhr, N.; Niewelt, B.: ¬Ein Retrievaltest mit automatisch indexierten Dokumenten (1984) 0.02 
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    Date
    20.10.2000 12:22:23
  3. Hlava, M.M.K.: Automatic indexing : comparing rule-based and statistics-based indexing systems (2005) 0.02 
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    Source
    Information outlook. 9(2005) no.8, S.22-23
  4. Mansour, N.; Haraty, R.A.; Daher, W.; Houri, M.: ¬An auto-indexing method for Arabic text (2008) 0.02 
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    Abstract
    This work addresses the information retrieval problem of auto-indexing Arabic documents. Auto-indexing a text document refers to automatically extracting words that are suitable for building an index for the document. In this paper, we propose an auto-indexing method for Arabic text documents. This method is mainly based on morphological analysis and on a technique for assigning weights to words. The morphological analysis uses a number of grammatical rules to extract stem words that become candidate index words. The weight assignment technique computes weights for these words relative to the container document. The weight is based on how spread is the word in a document and not only on its rate of occurrence. The candidate index words are then sorted in descending order by weight so that information retrievers can select the more important index words. We empirically verify the usefulness of our method using several examples. For these examples, we obtained an average recall of 46% and an average precision of 64%.
  5. Kempf, A.O.: Automatische Indexierung in der sozialwissenschaftlichen Fachinformation : eine Evaluationsstudie zur maschinellen Erschließung für die Datenbank SOLIS (2012) 0.02 
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    Content
    Vgl.: https://edoccluster.cms.hu-berlin.de/docviews/abstract.php?lang=ger&id=39543; http://http://edoc.hu-berlin.de/series/berliner-handreichungen/2012-329/PDF/329.pdf. Vgl. auch den Beitrag in: iwp 64(2013) H.2/3, S. 96-106.
  6. Willis, C.; Losee, R.M.: ¬A random walk on an ontology : using thesaurus structure for automatic subject indexing (2013) 0.02 
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    Content
    Korrektur einer Referenz in: JASIST 64(2013) no.8, S.1757.
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.7, S.1330-1344
  7. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.02 
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    Date
    14. 6.2015 22:12:44
  8. Hauer, M.: Automatische Indexierung (2000) 0.02 
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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) 0.02 
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    Date
    14. 6.2015 22:12:56
  10. Hauer, M.: Tiefenindexierung im Bibliothekskatalog : 17 Jahre intelligentCAPTURE (2019) 0.02 
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    Source
    B.I.T.online. 22(2019) H.2, S.163-166
  11. Kempf, A.O.: Automatische Inhaltserschließung in der Fachinformation (2013) 0.02 
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    Source
    Information - Wissenschaft und Praxis. 64(2013) H.2/3, S.96-106
  12. Biebricher, N.; Fuhr, N.; Lustig, G.; Schwantner, M.; Knorz, G.: ¬The automatic indexing system AIR/PHYS : from research to application (1988) 0.02 
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    Date
    16. 8.1998 12:51:22
  13. Kutschekmanesch, S.; Lutes, B.; Moelle, K.; Thiel, U.; Tzeras, K.: Automated multilingual indexing : a synthesis of rule-based and thesaurus-based methods (1998) 0.02 
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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
  14. Tsareva, P.V.: Algoritmy dlya raspoznavaniya pozitivnykh i negativnykh vkhozdenii deskriptorov v tekst i protsedura avtomaticheskoi klassifikatsii tekstov (1999) 0.02 
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    Date
    1. 4.2002 10:22:41
  15. Stankovic, R. et al.: Indexing of textual databases based on lexical resources : a case study for Serbian (2016) 0.02 
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    Date
    1. 2.2016 18:25:22
  16. Schneider, A.: Moderne Retrievalverfahren in klassischen bibliotheksbezogenen Anwendungen : Projekte und Perspektiven (2008) 0.02 
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    Pages
    64 S
  17. Kajanan, S.; Bao, Y.; Datta, A.; VanderMeer, D.; Dutta, K.: Efficient automatic search query formulation using phrase-level analysis (2014) 0.02 
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    Abstract
    Over the past decade, the volume of information available digitally over the Internet has grown enormously. Technical developments in the area of search, such as Google's Page Rank algorithm, have proved so good at serving relevant results that Internet search has become integrated into daily human activity. One can endlessly explore topics of interest simply by querying and reading through the resulting links. Yet, although search engines are well known for providing relevant results based on users' queries, users do not always receive the results they are looking for. Google's Director of Research describes clickstream evidence of frustrated users repeatedly reformulating queries and searching through page after page of results. Given the general quality of search engine results, one must consider the possibility that the frustrated user's query is not effective; that is, it does not describe the essence of the user's interest. Indeed, extensive research into human search behavior has found that humans are not very effective at formulating good search queries that describe what they are interested in. Ideally, the user should simply point to a portion of text that sparked the user's interest, and a system should automatically formulate a search query that captures the essence of the text. In this paper, we describe an implemented system that provides this capability. We first describe how our work differs from existing work in automatic query formulation, and propose a new method for improved quantification of the relevance of candidate search terms drawn from input text using phrase-level analysis. We then propose an implementable method designed to provide relevant queries based on a user's text input. We demonstrate the quality of our results and performance of our system through experimental studies. Our results demonstrate that our system produces relevant search terms with roughly two-thirds precision and recall compared to search terms selected by experts, and that typical users find significantly more relevant results (31% more relevant) more quickly (64% faster) using our system than self-formulated search queries. Further, we show that our implementation can scale to request loads of up to 10 requests per second within current online responsiveness expectations (<2-second response times at the highest loads tested).
  18. Tsujii, J.-I.: Automatic acquisition of semantic collocation from corpora (1995) 0.01 
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    Date
    31. 7.1996 9:22:19
  19. Riloff, E.: ¬An empirical study of automated dictionary construction for information extraction in three domains (1996) 0.01 
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    Date
    6. 3.1997 16:22:15
  20. Lepsky, K.; Vorhauer, J.: Lingo - ein open source System für die Automatische Indexierung deutschsprachiger Dokumente (2006) 0.01 
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    Date
    24. 3.2006 12:22:02

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