Search (35 results, page 1 of 2)

  • × theme_ss:"Automatisches Indexieren"
  1. Stegentritt, E.: Evaluationsresultate des mehrsprachigen Suchsystems CANAL/LS (1998) 0.06
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    Abstract
    The search system CANAL/LS simplifies the searching of library catalogues by analyzing search questions linguistically and translating them if required. The linguistic analysis reduces the search question words to their basic forms so that they can be compared with basic title forms. Consequently all variants of words and parts of compounds in German can be found. Presents the results of an analysis of search questions in a catalogue of 45.000 titles in the field of psychology
  2. Vinyals, O.; Toshev, A.; Bengio, S.; Erhan, D.: ¬A picture is worth a thousand (coherent) words : building a natural description of images (2014) 0.03
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    Content
    "People can summarize a complex scene in a few words without thinking twice. It's much more difficult for computers. But we've just gotten a bit closer -- we've developed a machine-learning system that can automatically produce captions (like the three above) to accurately describe images the first time it sees them. This kind of system could eventually help visually impaired people understand pictures, provide alternate text for images in parts of the world where mobile connections are slow, and make it easier for everyone to search on Google for images. Recent research has greatly improved object detection, classification, and labeling. But accurately describing a complex scene requires a deeper representation of what's going on in the scene, capturing how the various objects relate to one another and translating it all into natural-sounding language. Many efforts to construct computer-generated natural descriptions of images propose combining current state-of-the-art techniques in both computer vision and natural language processing to form a complete image description approach. But what if we instead merged recent computer vision and language models into a single jointly trained system, taking an image and directly producing a human readable sequence of words to describe it? This idea comes from recent advances in machine translation between languages, where a Recurrent Neural Network (RNN) transforms, say, a French sentence into a vector representation, and a second RNN uses that vector representation to generate a target sentence in German. Now, what if we replaced that first RNN and its input words with a deep Convolutional Neural Network (CNN) trained to classify objects in images? Normally, the CNN's last layer is used in a final Softmax among known classes of objects, assigning a probability that each object might be in the image. But if we remove that final layer, we can instead feed the CNN's rich encoding of the image into a RNN designed to produce phrases. We can then train the whole system directly on images and their captions, so it maximizes the likelihood that descriptions it produces best match the training descriptions for each image.
  3. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.03
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    Source
    Information processing and management. 22(1986) no.6, S.465-476
  4. Fuhr, N.; Niewelt, B.: ¬Ein Retrievaltest mit automatisch indexierten Dokumenten (1984) 0.02
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    Date
    20.10.2000 12:22:23
  5. 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
  6. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.02
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    Date
    14. 6.2015 22:12:44
  7. 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
  8. Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.02
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    Date
    14. 6.2015 22:12:56
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. Tsujii, J.-I.: Automatic acquisition of semantic collocation from corpora (1995) 0.01
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    Date
    31. 7.1996 9:22:19
  15. 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
  16. 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
  17. Probst, M.; Mittelbach, J.: Maschinelle Indexierung in der Sacherschließung wissenschaftlicher Bibliotheken (2006) 0.01
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    Date
    22. 3.2008 12:35:19
  18. Glaesener, L.: Automatisches Indexieren einer informationswissenschaftlichen Datenbank mit Mehrwortgruppen (2012) 0.01
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    Date
    11. 9.2012 19:43:22
  19. Hodges, P.R.: Keyword in title indexes : effectiveness of retrieval in computer searches (1983) 0.01
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    Date
    14. 3.1996 13:22:21
  20. Bordoni, L.; Pazienza, M.T.: Documents automatic indexing in an environmental domain (1997) 0.01
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    Source
    International forum on information and documentation. 22(1997) no.1, S.17-28