-
Schöneberg, U.; Gödert, W.: Erschließung mathematischer Publikationen mittels linguistischer Verfahren (2012)
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- Date
- 12. 9.2013 12:29:05
-
Franke-Maier, M.; Beck, C.; Kasprzik, A.; Maas, J.F.; Pielmeier, S.; Wiesenmüller, H: ¬Ein Feuerwerk an Algorithmen und der Startschuss zur Bildung eines Kompetenznetzwerks für maschinelle Erschließung : Bericht zur Fachtagung Netzwerk maschinelle Erschließung an der Deutschen Nationalbibliothek am 10. und 11. Oktober 2019 (2020)
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- Date
- 20. 2.2020 16:58:12
- Source
- o-bib: Das offene Bibliotheksjournal. 7(2020) Nr.1, S.1-12
-
Junger, U.; Schwens, U.: ¬Die inhaltliche Erschließung des schriftlichen kulturellen Erbes auf dem Weg in die Zukunft : Automatische Vergabe von Schlagwörtern in der Deutschen Nationalbibliothek (2017)
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- Date
- 19. 8.2017 9:24:22
-
Junger, U.: Can indexing be automated? : the example of the Deutsche Nationalbibliothek (2012)
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- Date
- 30. 5.2015 11:08:12
-
Mongin, L.; Fu, Y.Y.; Mostafa, J.: Open Archives data Service prototype and automated subject indexing using D-Lib archive content as a testbed (2003)
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- Source
- D-Lib magazine. 9(2003) no.12, x S
-
Mao, J.; Xu, W.; Yang, Y.; Wang, J.; Yuille, A.L.: Explain images with multimodal recurrent neural networks (2014)
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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.
-
Kiros, R.; Salakhutdinov, R.; Zemel, R.S.: Unifying visual-semantic embeddings with multimodal neural language models (2014)
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- Date
- 24. 5.2015 18:55:12
-
Pielmeier, S.; Voß, V.; Carstensen, H.; Kahl, B.: Online-Workshop "Computerunterstützte Inhaltserschließung" 2020 (2021)
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- Abstract
- Zum ersten Mal in digitaler Form und mit 230 Teilnehmer*innen fand am 11. und 12. November 2020 der 4. Workshop "Computerunterstützte Inhaltserschließung" statt, organisiert von der Deutschen Nationalbibliothek (DNB), der Firma Eurospider Information Technology, der Staatsbibliothek zu Berlin - Preußischer Kulturbesitz (SBB), der UB Stuttgart und dem Bibliotheksservice-Zentrum Baden-Württemberg (BSZ). Im Mittelpunkt stand der "Digitale Assistent DA-3": In elf Vorträgen wurden Anwendungsszenarien und Erfahrungen mit dem System vorgestellt, das Bibliotheken und andere Wissenschafts- und Kultureinrichtungen bei der Inhaltserschließung unterstützen soll. Die Begrüßung und Einführung in die beiden Workshop-Tage übernahm Frank Scholze (Generaldirektor der DNB). Er sieht den DA-3 als Baustein für die Verzahnung der intellektuellen und der maschinellen Erschließung.
-
Toepfer, M.; Kempf, A.O.: Automatische Indexierung auf Basis von Titeln und Autoren-Keywords : ein Werkstattbericht (2016)
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- Date
- 5. 5.2015 16:04:12
-
Toepfer, M.; Seifert, C.: Content-based quality estimation for automatic subject indexing of short texts under precision and recall constraints
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- Date
- 21. 6.2018 12:03:43
-
Markoff, J.: Researchers announce advance in image-recognition software (2014)
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- Date
- 24. 5.2015 12:27:20
