Search (9 results, page 1 of 1)

  • × language_ss:"e"
  • × theme_ss:"Computerlinguistik"
  • × year_i:[2020 TO 2030}
  1. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.06 
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  2. Morris, V.: Automated language identification of bibliographic resources (2020) 0.01 
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    Date
    2. 3.2020 19:04:22
  3. Luo, L.; Ju, J.; Li, Y.-F.; Haffari, G.; Xiong, B.; Pan, S.: ChatRule: mining logical rules with large language models for knowledge graph reasoning (2023) 0.01 
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    Date
    23.11.2023 19:07:22
  4. Hofstadter, D.: Artificial neural networks today are not conscious (2022) 0.00 
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  5. Agüera y Arcas, B.: Artificial neural networks are making strides towards consciousness (2022) 0.00 
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    Content
    Vgl. auch: Hofstadter, D.: Artificial neural networks today are not conscious.
  6. Al-Khatib, K.; Ghosa, T.; Hou, Y.; Waard, A. de; Freitag, D.: Argument mining for scholarly document processing : taking stock and looking ahead (2021) 0.00 
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  7. Aizawa, A.; Kohlhase, M.: Mathematical information retrieval (2021) 0.00 
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    Source
    Evaluating information retrieval and access tasks. Eds.: Sakai, T., Oard, D., Kando, N. [https://doi.org/10.1007/978-981-15-5554-1_12]
  8. Zaitseva, E.M.: Developing linguistic tools of thematic search in library information systems (2023) 0.00 
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    Abstract
    Within the R&D program "Information support of research by scientists and specialists on the basis of RNPLS&T Open Archive - the system of scientific knowledge aggregation", the RNPLS&T analyzes the use of linguistic tools of thematic search in the modern library information systems and the prospects for their development. The author defines the key common characteristics of e-catalogs of the largest Russian libraries revealed at the first stage of the analysis. Based on the specified common characteristics and detailed comparison analysis, the author outlines and substantiates the vectors for enhancing search inter faces of e-catalogs. The focus is made on linguistic tools of thematic search in library information systems; the key vectors are suggested: use of thematic search at different search levels with the clear-cut level differentiation; use of combined functionality within thematic search system; implementation of classification search in all e-catalogs; hierarchical representation of classifications; use of the matching systems for classification information retrieval languages, and in the long term classification and verbal information retrieval languages, and various verbal information retrieval languages. The author formulates practical recommendations to improve thematic search in library information systems.
  9. Brown, T.B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D.M.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, A.; Sutskever, I.; Amodei, D.: Language models are few-shot learners (2020) 0.00 
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