-
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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- Date
- 5. 9.2014 11:31:12
- 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
-
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
-
Lepsky, K.; Müller, T.; Wille, J.: Metadata improvement for image information retrieval (2010)
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- Series
- Advances in knowledge organization; vol.12
-
Junger, U.: Can indexing be automated? : the example of the Deutsche Nationalbibliothek (2012)
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- Date
- 30. 5.2015 11:08:12
-
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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- Date
- 6. 7.2022 19:14:12
-
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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- 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
-
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
-
Zhitomirsky-Geffet, M.; Prebor, G.; Bloch, O.: Improving proverb search and retrieval with a generic multidimensional ontology (2017)
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- Date
- 16.11.2017 12:55:10
-
Blank, I.; Rokach, L.; Shani, G.: Leveraging metadata to recommend keywords for academic papers (2016)
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- Source
- Journal of the Association for Information Science and Technology. 67(2016) no.12, S.3073-3091
-
Golub, K.; Soergel, D.; Buchanan, G.; Tudhope, D.; Lykke, M.; Hiom, D.: ¬A framework for evaluating automatic indexing or classification in the context of retrieval (2016)
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- Date
- 25.12.2015 11:12:47
-
Wang, S.; Koopman, R.: Embed first, then predict (2019)
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- Date
- 29. 9.2019 12:18:42
-
Markoff, J.: Researchers announce advance in image-recognition software (2014)
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- Date
- 24. 5.2015 12:27:20