Search (176 results, page 1 of 9)

  • × theme_ss:"Computerlinguistik"
  • × type_ss:"a"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.18
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    Content
    Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
    Date
    8. 1.2013 10:22:32
  2. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.08
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  3. Winograd, T.: ¬Ein prozedurales Modell des Sprachverstehens (1977) 0.06
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    Source
    Semantik und künstliche Intelligenz: Beiträge zur automatischen Sprachbearbeitung II. Hrsg. u. eingeleitet von P. Eisenberg
  4. Leighton, T.: ChatGPT und Künstliche Intelligenz : Utopie oder Dystopie? (2023) 0.05
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    Source
    https://www.heise.de/tp/features/ChatGPT-und-Kuenstliche-Intelligenz-Utopie-oder-Dystopie-7445181.html?view=print
  5. Schönbächler, E.; Strasser, T.; Himpsl-Gutermann, K.: Vom Chat zum Check : Informationskompetenz mit ChatGPT steigern (2023) 0.05
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    Abstract
    Der Beitrag greift den aktuellen Diskurs um die KI-Anwendung ChatGPT und deren Bedeutung in Schule und Hochschule auf. Dabei werden durch einen Überblick über verschiedene Assistenzsysteme, die auf Künstlicher Intelligenz beruhen, Grundlagen und Unterschiede herausgearbeitet. Der Bereich der Chatbots wird näher beleuchtet, die beiden grundlegenden Arten des regelbasierten Chatbots und des Machine Learning Bots werden anhand von anschaulichen Beispielen praxisnah erklärt. Schließlich wird herausgearbeitet, dass Informationskompetenz als Schlüsselkompetenz des 21. Jahrhunderts auch die wesentliche Grundlage dafür ist, im Bildungsbereich konstruktiv mit KI-Systemen wie ChatGPT umzugehen und die wesentlichen Funktionsmechanismen zu verstehen. Ein Unterrichtsentwurf zum Thema "Biene" schließt den Praxisbeitrag ab.
  6. ¬Der Student aus dem Computer (2023) 0.04
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    Abstract
    Kann gut zitieren, aber schlecht rechnen: Künstliche Intelligenz besteht Juraprüfung an US-Uni
    Date
    27. 1.2023 16:22:55
  7. Meng, K.; Ba, Z.; Ma, Y.; Li, G.: ¬A network coupling approach to detecting hierarchical linkages between science and technology (2024) 0.04
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    Abstract
    Detecting science-technology hierarchical linkages is beneficial for understanding deep interactions between science and technology (S&T). Previous studies have mainly focused on linear linkages between S&T but ignored their structural linkages. In this paper, we propose a network coupling approach to inspect hierarchical interactions of S&T by integrating their knowledge linkages and structural linkages. S&T knowledge networks are first enhanced with bidirectional encoder representation from transformers (BERT) knowledge alignment, and then their hierarchical structures are identified based on K-core decomposition. Hierarchical coupling preferences and strengths of the S&T networks over time are further calculated based on similarities of coupling nodes' degree distribution and similarities of coupling edges' weight distribution. Extensive experimental results indicate that our approach is feasible and robust in identifying the coupling hierarchy with superior performance compared to other isomorphism and dissimilarity algorithms. Our research extends the mindset of S&T linkage measurement by identifying patterns and paths of the interaction of S&T hierarchical knowledge.
  8. Pirkola, A.; Hedlund, T.; Keskustalo, H.; Järvelin, K.: Dictionary-based cross-language information retrieval : problems, methods, and research findings (2001) 0.04
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  9. Hartnett, K.: Sind Sprachmodelle bald die besseren Mathematiker? (2023) 0.03
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    Series
    Künstliche Intelligenz
  10. Strzalkowski, T.: Robust text processing in automated information retrieval (1994) 0.03
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.317-322.
  11. Janssen, J.-K.: ChatGPT-Klon läuft lokal auf jedem Rechner : Alpaca/LLaMA ausprobiert (2023) 0.03
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    Source
    https://www.heise.de/news/c-t-3003-ChatGPT-Klon-laeuft-lokal-auf-jedem-Rechner-Alpaca-LLaMA-ausprobiert-8004159.html?view=print
  12. Al-Khatib, K.; Ghosa, T.; Hou, Y.; Waard, A. de; Freitag, D.: Argument mining for scholarly document processing : taking stock and looking ahead (2021) 0.02
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  13. Ney, H.: Maschinelle Sprachverarbeitung und Statistik (1995) 0.02
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    Künstliche Intelligenz. 1995, H.5, S.27-33
  14. Radford, A.; Narasimhan, K.; Salimans, T.; Sutskever, I.: Improving language understanding by Generative Pre-Training 0.02
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    Semantik und künstliche Intelligenz: Beiträge zur automatischen Sprachbearbeitung II. Hsrg. u. eingeleitet von P. Eisenberg
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    Semantik und künstliche Intelligenz: Beiträge zur automatischen Sprachbearbeitung II. Hrsg. und eingeleitet von P. Eisenberg
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