-
Wang, S.; Koopman, R.: Embed first, then predict (2019)
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- Date
- 29. 9.2019 12:18:42
- Footnote
- Beitrag eines Special Issue: Research Information Systems and Science Classifications; including papers from "Trajectories for Research: Fathoming the Promise of the NARCIS Classification," 27-28 September 2018, The Hague, The Netherlands.
-
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
-
Souza, R.R.; Gil-Leiva, I.: Automatic indexing of scientific texts : a methodological comparison (2016)
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- Source
- Knowledge organization for a sustainable world: challenges and perspectives for cultural, scientific, and technological sharing in a connected society : proceedings of the Fourteenth International ISKO Conference 27-29 September 2016, Rio de Janeiro, Brazil / organized by International Society for Knowledge Organization (ISKO), ISKO-Brazil, São Paulo State University ; edited by José Augusto Chaves Guimarães, Suellen Oliveira Milani, Vera Dodebei
-
Munkelt, J.: Erstellung einer DNB-Retrieval-Testkollektion (2018)
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- Date
- 21. 6.2018 15:58:28
-
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.
-
Munkelt, J.; Schaer, P.; Lepsky, K.: Towards an IR test collection for the German National Library (2018)
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- Date
- 21. 6.2018 15:58:28
-
Banerjee, K.; Johnson, M.: Improving access to archival collections with automated entity extraction (2015)
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- Source
- Code4Lib journal. Issue 29(2015), [http://journal.code4lib.org/issues/issues/issue29]
-
Golub, K.; Lykke, M.; Tudhope, D.: Enhancing social tagging with automated keywords from the Dewey Decimal Classification (2014)
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- Abstract
- Purpose - The purpose of this paper is to explore the potential of applying the Dewey Decimal Classification (DDC) as an established knowledge organization system (KOS) for enhancing social tagging, with the ultimate purpose of improving subject indexing and information retrieval. Design/methodology/approach - Over 11.000 Intute metadata records in politics were used. Totally, 28 politics students were each given four tasks, in which a total of 60 resources were tagged in two different configurations, one with uncontrolled social tags only and another with uncontrolled social tags as well as suggestions from a controlled vocabulary. The controlled vocabulary was DDC comprising also mappings from the Library of Congress Subject Headings. Findings - The results demonstrate the importance of controlled vocabulary suggestions for indexing and retrieval: to help produce ideas of which tags to use, to make it easier to find focus for the tagging, to ensure consistency and to increase the number of access points in retrieval. The value and usefulness of the suggestions proved to be dependent on the quality of the suggestions, both as to conceptual relevance to the user and as to appropriateness of the terminology. Originality/value - No research has investigated the enhancement of social tagging with suggestions from the DDC, an established KOS, in a user trial, comparing social tagging only and social tagging enhanced with the suggestions. This paper is a final reflection on all aspects of the study.
-
Vinyals, O.; Toshev, A.; Bengio, S.; Erhan, D.: Show and tell : a neural image caption generator (2014)
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- Abstract
- Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this paper, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image. The model is trained to maximize the likelihood of the target description sentence given the training image. Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. Our model is often quite accurate, which we verify both qualitatively and quantitatively. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69. We also show BLEU-1 score improvements on Flickr30k, from 56 to 66, and on SBU, from 19 to 28. Lastly, on the newly released COCO dataset, we achieve a BLEU-4 of 27.7, which is the current state-of-the-art.
-
Husevag, A.-S.R.: Named entities in indexing : a case study of TV subtitles and metadata records (2016)
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- Date
- 16. 9.2016 19:00:28
-
Li, X.; Zhang, A.; Li, C.; Ouyang, J.; Cai, Y.: Exploring coherent topics by topic modeling with term weighting (2018)
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- Date
- 15. 3.2019 18:55:29
-
Willis, C.; Losee, R.M.: ¬A random walk on an ontology : using thesaurus structure for automatic subject indexing (2013)
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- Date
- 28. 7.2013 14:20:39
-
Tavakolizadeh-Ravari, M.: Analysis of the long term dynamics in thesaurus developments and its consequences (2017)
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- Date
- 24. 8.2016 13:45:28
-
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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- 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
-
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
