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  • × theme_ss:"Automatisches Indexieren"
  1. 7e Dag van het Document : 19 & 20 mei 1998, Congrescentrum De Reehorst, Ede ; proceedings (1998) 0.05 
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  2. Biebricher, N.; Fuhr, N.; Lustig, G.; Schwantner, M.; Knorz, G.: ¬The automatic indexing system AIR/PHYS : from research to application (1988) 0.04 
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    16. 8.1998 12:51:22
  3. Salton, G.; McGill, M. J.: Introduction to modern information retrieval (1983) 0.02 
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  4. Sparck Jones, K.: Automatic keyword classification for information retrieval (1971) 0.02 
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  5. Tavakolizadeh-Ravari, M.: Analysis of the long term dynamics in thesaurus developments and its consequences (2017) 0.02 
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    Content
    Vgl.: https://www.ibi.hu-berlin.de/de/archiv/forschung/prom_habil/dissertationen/Tavakolizadeh-Ravari2007. Vgl. auch: http://mravari.blogfa.com/post-20.aspxgl.
  6. Martins, A.L.; Souza, R.R.; Ribeiro de Mello, H.: ¬The use of noun phrases in information retrieval : proposing a mechanism for automatic classification (2014) 0.02 
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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
  7. Lepsky, K.: Automatische Indexierung in der Inhaltserschließung (1998) 0.02 
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    Source
    7e dag van het document, 19 & 20 mei 1998. Congrescentrum De Reehorst, Ede. Red.: J.R. Creutzberg
  8. ¬The smart retrieval system : experiments in automatic document processing (1971) 0.02 
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  9. Borko, H.; Bernick, M.: Automatic document classification : T.2 (1964) 0.02 
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  10. Borko, H.; Bernick, M.: Automatic document classification : T.1 (1963) 0.02 
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  11. Greiner-Petter, A.; Schubotz, M.; Cohl, H.S.; Gipp, B.: Semantic preserving bijective mappings for expressions involving special functions between computer algebra systems and document preparation systems (2019) 0.02 
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    Date
    20. 1.2015 18:30:22
  12. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.02 
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    Information processing and management. 22(1986) no.6, S.465-476
  13. Hlava, M.M.K.: Automatic indexing : comparing rule-based and statistics-based indexing systems (2005) 0.01 
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    Information outlook. 9(2005) no.8, S.22-23
  14. Thirion, B.; Leroy, J.P.; Baudic, F.; Douyère, M.; Piot, J.; Darmoni, S.J.: SDI selecting, decribing, and indexing : did you mean automatically? (2001) 0.01 
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  15. Souza, R.R.; Gil-Leiva, I.: Automatic indexing of scientific texts : a methodological comparison (2016) 0.01 
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    Knowledge organization for a sustainable world: challenges and perspectives for cultural, scientific, and technological sharing in a connected society : proceedings of the Fourteenth International ISKO Conference 27-29 September 2016, Rio de Janeiro, Brazil / organized by International Society for Knowledge Organization (ISKO), ISKO-Brazil, São Paulo State University ; edited by José Augusto Chaves Guimarães, Suellen Oliveira Milani, Vera Dodebei
  16. Williams, R.V.: Hans Peter Luhn and Herbert M. Ohlman : their roles in the origins of keyword-in-context/permutation automatic indexing (2010) 0.01 
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    Biographed
    Ohlmann, Herbert M.
  17. Mao, J.; Xu, W.; Yang, Y.; Wang, J.; Yuille, A.L.: Explain images with multimodal recurrent neural networks (2014) 0.01 
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    Abstract
    In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel sentence descriptions to explain the content of images. It directly models the probability distribution of generating a word given previous words and the image. Image descriptions are generated by sampling from this distribution. The model consists of two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. These two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of our model is validated on three benchmark datasets (IAPR TC-12 [8], Flickr 8K [28], and Flickr 30K [13]). Our model outperforms the state-of-the-art generative method. In addition, the m-RNN model can be applied to retrieval tasks for retrieving images or sentences, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval.
  18. Salton, G.: Automatic text processing : the transformation, analysis, and retrieval of information by computer (1989) 0.01 
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  19. Moens, M.F.: Automatic indexing and abstracting of document texts (2000) 0.01 
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  20. Kutschekmanesch, S.; Lutes, B.; Moelle, K.; Thiel, U.; Tzeras, K.: Automated multilingual indexing : a synthesis of rule-based and thesaurus-based methods (1998) 0.01 
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    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

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