Search (58 results, page 2 of 3)

  • × theme_ss:"Computerlinguistik"
  • × type_ss:"el"
  1. Ramisch, C.; Schreiner, P.; Idiart, M.; Villavicencio, A.: ¬An evaluation of methods for the extraction of multiword expressions (20xx) 0.00 
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  2. Kiela, D.; Clark, S.: Detecting compositionality of multi-word expressions using nearest neighbours in vector space models (2013) 0.00 
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  3. Nielsen, R.D.; Ward, W.; Martin, J.H.; Palmer, M.: Extracting a representation from text for semantic analysis (2008) 0.00 
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  4. ChatGPT : Optimizing language models for dalogue (2022) 0.00 
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  5. Lund, B.D.: ¬A chat with ChatGPT : how will AI impact scholarly publishing? (2022) 0.00 
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  6. Deutsche Forschungsgemeinschaft: Stellungnahme des Präsidiums der Deutschen Forschungsgemeinschaft (DFG) zum Einfluss generativer Modelle für die Text- und Bilderstellung auf die Wissenschaften und das Förderhandeln der DFG (2023) 0.00 
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    Abstract
    Die Einsatzmöglichkeiten von "Künstlicher Intelligenz" (KI) beschäftigen derzeit große Teile der Gesellschaft. Anlass ist insbesondere die Entwicklung generativer Modelle für die Text- und Bilderstellung wie "ChatGPT" und "DALL-E", die eine Interaktion zwischen Mensch und technischem System in gesprochener oder Text-/Bildsprache ermöglichen, und ihre Bereitstellung für die Allgemeinheit.
  7. Wordhoard (o.J.) 0.00 
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  8. Rindflesch, T.C.; Aronson, A.R.: Semantic processing in information retrieval (1993) 0.00 
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  9. Wong, W.; Liu, W.; Bennamoun, M.: Ontology learning from text : a look back and into the future (2010) 0.00 
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  10. Shen, M.; Liu, D.-R.; Huang, Y.-S.: Extracting semantic relations to enrich domain ontologies (2012) 0.00 
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  11. Dias, G.: Multiword unit hybrid extraction (o.J.) 0.00 
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  12. WordHoard: finding multiword units (20??) 0.00 
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  13. Aizawa, A.; Kohlhase, M.: Mathematical information retrieval (2021) 0.00 
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  14. Weiß, E.-M.: ChatGPT soll es richten : Microsoft baut KI in Suchmaschine Bing ein (2023) 0.00 
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  15. Stieler, W.: Anzeichen von Bewusstsein bei ChatGPT und Co.? (2023) 0.00 
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    Abstract
    Ein interdisziplinäres Forschungsteam hat eine Liste von Eigenschaften aufgestellt, die auf Bewusstsein deuten, und aktuelle KI-Systeme darauf abgeklopft. Ein interdisziplinäres Forscherteam hat ein Paper [https://arxiv.org/abs/2308.08708] veröffentlicht, das eine Liste von 14 "Indikatoren" für Bewusstsein enthält, die aus sechs aktuellen Theorien über das Bewusstsein stammen. Aktuelle KI-Modelle wie GPT-3, Palm-E oder AdA von Deepmind weisen demnach einzelne dieser Indikatoren auf. "Es spricht viel dafür, dass die meisten oder alle Bedingungen für das Bewusstsein, die von derzeitigen Theorien vorgeschlagenen Bedingungen für das Bewusstsein mit den bestehenden Techniken der KI erfüllt werden können", schreiben die Autoren. Zum Team gehörte auch der Deep-Learning-Pionier Yoshua Bengio von der Université de Montréal.
  16. Galitsky, B.: Can many agents answer questions better than one? (2005) 0.00 
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  17. Sebastiani, F.: ¬A tutorial an automated text categorisation (1999) 0.00 
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  18. Dunning, T.: Statistical identification of language (1994) 0.00 
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  19. Spitkovsky, V.I.; Chang, A.X.: ¬A cross-lingual dictionary for english Wikipedia concepts (2012) 0.00 
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  20. Chowdhury, A.; Mccabe, M.C.: Improving information retrieval systems using part of speech tagging (1993) 0.00 
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