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  • × theme_ss:"Automatisches Indexieren"
  1. Kumpe, D.: Methoden zur automatischen Indexierung von Dokumenten (2006) 0.01 
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    Abstract
    Diese Diplomarbeit handelt von der Indexierung von unstrukturierten und natürlichsprachigen Dokumenten. Die zunehmende Informationsflut und die Zahl an veröffentlichten wissenschaftlichen Berichten und Büchern machen eine maschinelle inhaltliche Erschließung notwendig. Um die Anforderungen hierfür besser zu verstehen, werden Probleme der natürlichsprachigen schriftlichen Kommunikation untersucht. Die manuellen Techniken der Indexierung und die Dokumentationssprachen werden vorgestellt. Die Indexierung wird thematisch in den Bereich der inhaltlichen Erschließung und des Information Retrieval eingeordnet. Weiterhin werden Vor- und Nachteile von ausgesuchten Algorithmen untersucht und Softwareprodukte im Bereich des Information Retrieval auf ihre Arbeitsweise hin evaluiert. Anhand von Beispiel-Dokumenten werden die Ergebnisse einzelner Verfahren vorgestellt. Mithilfe des Projekts European Migration Network werden Probleme und grundlegende Anforderungen an die Durchführung einer inhaltlichen Erschließung identifiziert und Lösungsmöglichkeiten vorgeschlagen.
  2. Salton, G.: Another look at automatic text-retrieval systems (1986) 0.01 
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    Source
    Communications of the Association for Computing Machinery. 29(1986), S.648-656
  3. Salton, G.; Allan, J.; Buckley, C.; Singhal, A.: Automatic analysis, theme generation, and summarization of machine readable texts (1994) 0.01 
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    Date
    16. 8.1998 12:30:29
  4. Hüther, H.: Selix im DFG-Projekt Kascade (1998) 0.01 
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    Date
    25. 8.2000 19:55:29
  5. Biebricher, N.; Fuhr, N.; Lustig, G.; Schwantner, M.; Knorz, G.: ¬The automatic indexing system AIR/PHYS : from research to application (1988) 0.01 
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    Date
    16. 8.1998 12:51:22
  6. Kutschekmanesch, S.; Lutes, B.; Moelle, K.; Thiel, U.; Tzeras, K.: Automated multilingual indexing : a synthesis of rule-based and thesaurus-based methods (1998) 0.01 
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    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
  7. Tsareva, P.V.: Algoritmy dlya raspoznavaniya pozitivnykh i negativnykh vkhozdenii deskriptorov v tekst i protsedura avtomaticheskoi klassifikatsii tekstov (1999) 0.01 
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    Date
    1. 4.2002 10:22:41
  8. Stankovic, R. et al.: Indexing of textual databases based on lexical resources : a case study for Serbian (2016) 0.01 
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    Date
    1. 2.2016 18:25:22
  9. Goller, C.; Löning, J.; Will, T.; Wolff, W.: Automatic document classification : a thourough evaluation of various methods (2000) 0.01 
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    Abstract
    (Automatic) document classification is generally defined as content-based assignment of one or more predefined categories to documents. Usually, machine learning, statistical pattern recognition, or neural network approaches are used to construct classifiers automatically. In this paper we thoroughly evaluate a wide variety of these methods on a document classification task for German text. We evaluate different feature construction and selection methods and various classifiers. Our main results are: (1) feature selection is necessary not only to reduce learning and classification time, but also to avoid overfitting (even for Support Vector Machines); (2) surprisingly, our morphological analysis does not improve classification quality compared to a letter 5-gram approach; (3) Support Vector Machines are significantly better than all other classification methods
  10. Karpathy, A.; Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions (2015) 0.01 
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    Abstract
    We present a model that generates free-form natural language descriptions of image regions. Our model leverages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between text and visual data. Our approach is based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding. We then describe a Recurrent Neural Network architecture that uses the inferred alignments to learn to generate novel descriptions of image regions. We demonstrate the effectiveness of our alignment model with ranking experiments on Flickr8K, Flickr30K and COCO datasets, where we substantially improve on the state of the art. We then show that the sentences created by our generative model outperform retrieval baselines on the three aforementioned datasets and a new dataset of region-level annotations.
  11. Kiros, R.; Salakhutdinov, R.; Zemel, R.S.: Unifying visual-semantic embeddings with multimodal neural language models (2014) 0.01 
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    Abstract
    Inspired by recent advances in multimodal learning and machine translation, we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space. Our pipeline effectively unifies joint image-text embedding models with multimodal neural language models. We introduce the structure-content neural language model that disentangles the structure of a sentence to its content, conditioned on representations produced by the encoder. The encoder allows one to rank images and sentences while the decoder can generate novel descriptions from scratch. Using LSTM to encode sentences, we match the state-of-the-art performance on Flickr8K and Flickr30K without using object detections. We also set new best results when using the 19-layer Oxford convolutional network. Furthermore we show that with linear encoders, the learned embedding space captures multimodal regularities in terms of vector space arithmetic e.g. *image of a blue car* - "blue" + "red" is near images of red cars. Sample captions generated for 800 images are made available for comparison.
  12. 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) 0.01 
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    Abstract
    Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or "temporally deep", are effective for tasks involving sequences, visual and otherwise. We develop a novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and demonstrate the value of these models on benchmark video recognition tasks, image description and retrieval problems, and video narration challenges. In contrast to current models which assume a fixed spatio-temporal receptive field or simple temporal averaging for sequential processing, recurrent convolutional models are "doubly deep" in that they can be compositional in spatial and temporal "layers". Such models may have advantages when target concepts are complex and/or training data are limited. Learning long-term dependencies is possible when nonlinearities are incorporated into the network state updates. Long-term RNN models are appealing in that they directly can map variable-length inputs (e.g., video frames) to variable length outputs (e.g., natural language text) and can model complex temporal dynamics; yet they can be optimized with backpropagation. Our recurrent long-term models are directly connected to modern visual convnet models and can be jointly trained to simultaneously learn temporal dynamics and convolutional perceptual representations. Our results show such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined and/or optimized.
  13. Koryconski, C.; Newell, A.F.: Natural-language processing and automatic indexing (1990) 0.01 
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    Source
    Indexer. 17(1990), S.21-29
  14. Frants, V.I.; Kamenoff, N.I.; Shapiro, J.: ¬One approach to classification of users and automatic clustering of documents (1993) 0.01 
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    Information processing and management. 29(1993) no.2, S.187-195
  15. Haas, S.; He, S.: Toward the automatic identification of sublanguage vocabulary (1993) 0.01 
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    Information processing and management. 29(1993) no.6, S.721-744
  16. Molto, M.: Improving full text search performance through textual analysis (1993) 0.01 
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    Information processing and management. 29(1993) no.5, S.614-632
  17. Damerau, F.J.: Generating an evaluating domain-oriented multi-word terms from texts (1993) 0.01 
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    Information processing and management. 29(1993) no.4, S.433-447
  18. Konings, E.; Gramsbergen, E.: Automatische onderwerpsondexering van een bibliotheekscatalogus : Ervaringen van de Bibliotheek TU Delft (1999) 0.01 
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    Date
    26. 1.2008 17:29:51
  19. Kim, P.K.: ¬An automatic indexing of compound words based on mutual information for Korean text retrieval (1995) 0.01 
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    Library and information science. 1995, no.34, S.29-38
  20. Souza, R.R.; Gil-Leiva, I.: Automatic indexing of scientific texts : a methodological comparison (2016) 0.01 
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    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

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Years

Languages

  • e 41
  • d 29
  • ja 2
  • nl 2
  • ru 1
  • More… Less…

Types

  • a 66
  • el 9
  • x 4
  • m 2
  • p 1
  • More… Less…

Themes

Classifications