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Stankovic, R. et al.: Indexing of textual databases based on lexical resources : a case study for Serbian (2016)
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- Date
- 1. 2.2016 18:25:22
- Pages
- S.167-181
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Simões, M. da Graça; Machado, L.M.; Souza, R.R.; Almeida, M.B.; Tavares Lopes, A.: Automatic indexing and ontologies : the consistency of research chronology and authoring in the context of Information Science (2018)
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Junger, U.: Can indexing be automated? : the example of the Deutsche Nationalbibliothek (2012)
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Junger, U.: Can indexing be automated? : the example of the Deutsche Nationalbibliothek (2014)
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Siebenkäs, A.; Markscheffel, B.: Conception of a workflow for the semi-automatic construction of a thesaurus for the German printing industry (2015)
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Lepsky, K.; Müller, T.; Wille, J.: Metadata improvement for image information retrieval (2010)
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Lichtenstein, A.; Plank, M.; Neumann, J.: TIB's portal for audiovisual media : combining manual and automatic indexing (2014)
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Munkelt, J.: Erstellung einer DNB-Retrieval-Testkollektion (2018)
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- Pages
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Donahue, J.; Hendricks, L.A.; Guadarrama, S.; Rohrbach, M.; Venugopalan, S.; Saenko, K.; Darrell, T.: Long-term recurrent convolutional networks for visual recognition and description (2014)
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Lu, K.; Mao, J.; Li, G.: Toward effective automated weighted subject indexing : a comparison of different approaches in different environments (2018)
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- Footnote
- Vgl. das Erratum in JASIST 69(2018) no.7, S.956.
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- Journal of the Association for Information Science and Technology. 69(2018) no.1, S.121-133
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Lu, K.; Mao, J.: ¬An automatic approach to weighted subject indexing : an empirical study in the biomedical domain (2015)
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- Journal of the Association for Information Science and Technology. 66(2015) no.9, S.1776-1784
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Li, X.; Zhang, A.; Li, C.; Ouyang, J.; Cai, Y.: Exploring coherent topics by topic modeling with term weighting (2018)
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- Information processing and management. 54(2018) no.6, S.1345-1358
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Mao, J.; Xu, W.; Yang, Y.; Wang, J.; Yuille, A.L.: Explain images with multimodal recurrent neural networks (2014)
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Gábor, K.; Zargayouna, H.; Tellier, I.; Buscaldi, D.; Charnois, T.: ¬A typology of semantic relations dedicated to scientific literature analysis (2016)
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- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Munkelt, J.; Schaer, P.; Lepsky, K.: Towards an IR test collection for the German National Library (2018)
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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)
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- Pages
- S.320-326
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- 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
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Mesquita, L.A.P.; Souza, R.R.; Baracho Porto, R.M.A.: Noun phrases in automatic indexing: : a structural analysis of the distribution of relevant terms in doctoral theses (2014)
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- Pages
- S.327-334
- 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
-
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)
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- Date
- 20. 1.2015 18:30:22
- Source
- Aslib journal of information management. 71(2019) no.3, S.415-439
-
Markoff, J.: Researchers announce advance in image-recognition software (2014)
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- Content
- Computer vision specialists said that despite the improvements, these software systems had made only limited progress toward the goal of digitally duplicating human vision and, even more elusive, understanding. "I don't know that I would say this is 'understanding' in the sense we want," said John R. Smith, a senior manager at I.B.M.'s T.J. Watson Research Center in Yorktown Heights, N.Y. "I think even the ability to generate language here is very limited." But the Google and Stanford teams said that they expect to see significant increases in accuracy as they improve their software and train these programs with larger sets of annotated images. A research group led by Tamara L. Berg, a computer scientist at the University of North Carolina at Chapel Hill, is training a neural network with one million images annotated by humans. "You're trying to tell the story behind the image," she said. "A natural scene will be very complex, and you want to pick out the most important objects in the image.""
-
Williams, R.V.: Hans Peter Luhn and Herbert M. Ohlman : their roles in the origins of keyword-in-context/permutation automatic indexing (2010)
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- Source
- Journal of the American Society for Information Science and Technology. 61(2010) no.4, S.835-849
