Search (217 results, page 11 of 11)

  • × language_ss:"e"
  • × theme_ss:"Automatisches Indexieren"
  1. Vlachidis, A.; Tudhope, D.: ¬A knowledge-based approach to information extraction for semantic interoperability in the archaeology domain (2016) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.5, S.1138-1152
  2. Husevag, A.-S.R.: Named entities in indexing : a case study of TV subtitles and metadata records (2016) 0.00
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    Pages
    S.48-58
  3. Kanan, T.; Fox, E.A.: Automated arabic text classification with P-Stemmer, machine learning, and a tailored news article taxonomy (2016) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.11, S.2667-2683
  4. Blank, I.; Rokach, L.; Shani, G.: Leveraging metadata to recommend keywords for academic papers (2016) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.12, S.3073-3091
  5. 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) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.1, S.3-16
  6. Smiraglia, R.P.; Cai, X.: Tracking the evolution of clustering, machine learning, automatic indexing and automatic classification in knowledge organization (2017) 0.00
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    Source
    Knowledge organization. 44(2017) no.3, S.215-233
  7. Lu, K.; Mao, J.: ¬An automatic approach to weighted subject indexing : an empirical study in the biomedical domain (2015) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.9, S.1776-1784
  8. Li, X.; Zhang, A.; Li, C.; Ouyang, J.; Cai, Y.: Exploring coherent topics by topic modeling with term weighting (2018) 0.00
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    Source
    Information processing and management. 54(2018) no.6, S.1345-1358
  9. Golub, K.: Automatic subject indexing of text (2019) 0.00
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    Source
    Knowledge organization. 46(2019) no.2, S.104-121
  10. Zhang, Y.; Zhang, C.; Li, J.: Joint modeling of characters, words, and conversation contexts for microblog keyphrase extraction (2020) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 71(2020) no.5, S.553-567
  11. Ahmed, M.: Automatic indexing for agriculture : designing a framework by deploying Agrovoc, Agris and Annif (2023) 0.00
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    Source
    ¬SRELS Journal of Information Management. 60(2023) no.2, S.85-95
  12. Needham, R.M.; Sparck Jones, K.: Keywords and clumps (1985) 0.00
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    Footnote
    Original in: Journal of documentation 20(1964) no.1, S.5-15.
    Pages
    S.262-272
  13. Tavakolizadeh-Ravari, M.: Analysis of the long term dynamics in thesaurus developments and its consequences (2017) 0.00
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    Pages
    128 S
  14. SIGIR'92 : Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (1992) 0.00
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    Type
    s
  15. Rasmussen, E.M.: Indexing and retrieval for the Web (2002) 0.00
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    Source
    Annual review of information science and technology. 37(2003), S.91-126
  16. Vinyals, O.; Toshev, A.; Bengio, S.; Erhan, D.: ¬A picture is worth a thousand (coherent) words : building a natural description of images (2014) 0.00
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  17. Markoff, J.: Researchers announce advance in image-recognition software (2014) 0.00
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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.""

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