Search (11 results, page 1 of 1)

  • × author_ss:"Wang, J."
  1. He, R.; Wang, J.; Tian, J.; Chu, C.-T.; Mauney, B.; Perisic, I.: Session analysis of people search within a professional social network (2013) 0.00
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    Date
    19. 4.2013 20:31:22
  2. Strzalkowski, T.; Guthrie, L.; Karlgren, J.; Leistensnider, J.; Lin, F.; Perez-Carballo, J.; Straszheim, T.; Wang, J.; Wilding, J.: Natural language information retrieval : TREC-5 report (1997) 0.00
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  3. Hicks, D.; Wang, J.: Coverage and overlap of the new social sciences and humanities journal lists (2011) 0.00
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    Date
    22. 1.2011 13:21:28
  4. Shen, R.; Wang, J.; Fox, E.A.: ¬A Lightweight Protocol between Digital Libraries and Visualization Systems (2002) 0.00
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    Date
    22. 2.2003 17:25:39
    22. 2.2003 18:15:14
  5. Wang, J.: Automatic thesaurus development : term extraction from title metadata (2006) 0.00
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    Abstract
    The application of thesauri in networked environments is seriously hampered by the challenges of introducing new concepts and terminology into the formal controlled vocabulary, which is critical for enhancing its retrieval capability. The author describes an automated process of adding new terms to thesauri as entry vocabulary by analyzing the association between words/phrases extracted from bibliographic titles and subject descriptors in the metadata record (subject descriptors are terms assigned from controlled vocabularies of thesauri to describe the subjects of the objects [e.g., books, articles] represented by the metadata records). The investigated approach uses a corpus of metadata for scientific and technical (S&T) publications in which the titles contain substantive words for key topics. The three steps of the method are (a) extracting words and phrases from the title field of the metadata; (b) applying a method to identify and select the specific and meaningful keywords based on the associated controlled vocabulary terms from the thesaurus used to catalog the objects; and (c) inserting selected keywords into the thesaurus as new terms (most of them are in hierarchical relationships with the existing concepts), thereby updating the thesaurus with new terminology that is being used in the literature. The effectiveness of the method was demonstrated by an experiment with the Chinese Classification Thesaurus (CCT) and bibliographic data in China Machine-Readable Cataloging Record (MARC) format (CNMARC) provided by Peking University Library. This approach is equally effective in large-scale collections and in other languages.
  6. Wang, J.; Guan, J.: ¬The analysis and evaluation of knowledge efficiency in research groups (2005) 0.00
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    Date
    22.10.2005 18:42:28
  7. Oard, D.W.; He, D.; Wang, J.: User-assisted query translation for interactive cross-language information retrieval (2008) 0.00
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    Date
    28. 7.2008 15:08:49
  8. Mao, J.; Xu, W.; Yang, Y.; Wang, J.; Yuille, A.L.: Explain images with multimodal recurrent neural networks (2014) 0.00
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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.
  9. Thomas, I.S.; Wang, J.; GPT-3: Was euch zu Menschen macht : Antworten einer künstlichen Intelligenz auf die großen Fragen des Lebens (2022) 0.00
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    Date
    7. 1.2023 18:41:29
  10. Jiang, Z.; Gu, Q.; Yin, Y.; Wang, J.; Chen, D.: GRAW+ : a two-view graph propagation method with word coupling for readability assessment (2019) 0.00
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    Date
    15. 4.2019 13:46:22
  11. Wang, J.; Halffman, W.; Zhang, Y.H.: Sorting out journals : the proliferation of journal lists in China (2023) 0.00
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    Date
    22. 9.2023 16:39:23