Search (75 results, page 1 of 4)

  • × theme_ss:"Automatisches Indexieren"
  1. Wolfekuhler, M.R.; Punch, W.F.: Finding salient features for personal Web pages categories (1997) 0.02
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    Date
    1. 8.1996 22:08:06
    Source
    Computer networks and ISDN systems. 29(1997) no.8, S.1147-1156
  2. Franke-Maier, M.: Anforderungen an die Qualität der Inhaltserschließung im Spannungsfeld von intellektuell und automatisch erzeugten Metadaten (2018) 0.02
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    Abstract
    Spätestens seit dem Deutschen Bibliothekartag 2018 hat sich die Diskussion zu den automatischen Verfahren der Inhaltserschließung der Deutschen Nationalbibliothek von einer politisch geführten Diskussion in eine Qualitätsdiskussion verwandelt. Der folgende Beitrag beschäftigt sich mit Fragen der Qualität von Inhaltserschließung in digitalen Zeiten, wo heterogene Erzeugnisse unterschiedlicher Verfahren aufeinandertreffen und versucht, wichtige Anforderungen an Qualität zu definieren. Dieser Tagungsbeitrag fasst die vom Autor als Impulse vorgetragenen Ideen beim Workshop der FAG "Erschließung und Informationsvermittlung" des GBV am 29. August 2018 in Kiel zusammen. Der Workshop fand im Rahmen der 22. Verbundkonferenz des GBV statt.
  3. Kuhlen, R.: Morphologische Relationen durch Reduktionsalgorithmen (1974) 0.01
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    Date
    29. 1.2011 14:56:29
  4. 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.
  5. Blank, I.; Rokach, L.; Shani, G.: Leveraging metadata to recommend keywords for academic papers (2016) 0.01
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    Abstract
    Users of research databases, such as CiteSeerX, Google Scholar, and Microsoft Academic, often search for papers using a set of keywords. Unfortunately, many authors avoid listing sufficient keywords for their papers. As such, these applications may need to automatically associate good descriptive keywords with papers. When the full text of the paper is available this problem has been thoroughly studied. In many cases, however, due to copyright limitations, research databases do not have access to the full text. On the other hand, such databases typically maintain metadata, such as the title and abstract and the citation network of each paper. In this paper we study the problem of predicting which keywords are appropriate for a research paper, using different methods based on the citation network and available metadata. Our main goal is in providing search engines with the ability to extract keywords from the available metadata. However, our system can also be used for other applications, such as for recommending keywords for the authors of new papers. We create a data set of research papers, and their citation network, keywords, and other metadata, containing over 470K papers with and more than 2 million keywords. We compare our methods with predicting keywords using the title and abstract, in offline experiments and in a user study, concluding that the citation network provides much better predictions.
  6. Panyr, J.: STEINADLER: ein Verfahren zur automatischen Deskribierung und zur automatischen thematischen Klassifikation (1978) 0.01
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    Source
    Nachrichten für Dokumentation. 29(1978), S.92-96
  7. Salton, G.; Yang, C.S.: On the specification of term values in automatic indexing (1973) 0.01
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    Source
    Journal of documentation. 29(1973), S.351-372
  8. 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
  9. Fuhr, N.; Niewelt, B.: ¬Ein Retrievaltest mit automatisch indexierten Dokumenten (1984) 0.01
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    Date
    20.10.2000 12:22:23
  10. 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
  11. Alexander, M.: Retrieving digital data with fuzzy matching (1997) 0.01
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    Abstract
    In 1993 the British Library established a programme of activities entitled Initiatives for Access (IFA) to identify and develop computer applications based on the new technologies emerging in the aereas of digital and network service. Discusses the problem of the effective retrieval of digital data after its capture focusing on the product Excalibur EFS which looks at the way information is sorted at its fundamental level and identifies patterns in numbers. Looks at the benefits of Excalibur and outlines other experiments in progress as part of the IFA programme
  12. Hirawa, M.: Role of keywords in the network searching era (1998) 0.01
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  13. Junger, U.: Möglichkeiten und Probleme automatischer Erschließungsverfahren in Bibliotheken : Bericht vom KASCADE-Workshop in der Universitäts- und Landesbibliothek Düsseldorf (1999) 0.01
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    Date
    23.10.1996 17:26:29
  14. Spitters, M.J.: Adjust : automatische thesauriele ontsluiting van grote hoeveelheden krantenartikelen (1999) 0.01
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    Source
    Informatie professional. 3(1999) no.10, S.29-31
  15. Knorz, G.: Automatische Indexierung (1994) 0.01
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    Date
    29. 1.2011 17:56:21
  16. Nicoletti, M.: Automatische Indexierung (2001) 0.01
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    Date
    29. 9.2017 12:00:04
  17. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.01
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    Date
    14. 6.2015 22:12:44
  18. 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
  19. Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.01
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    Date
    14. 6.2015 22:12:56
  20. 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

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