Search (14 results, page 1 of 1)

  • × theme_ss:"Computerlinguistik"
  • × type_ss:"el"
  1. Boleda, G.; Evert, S.: Multiword expressions : a pain in the neck of lexical semantics (2009) 0.02 
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    Date
    1. 3.2013 14:56:22
  2. Heaven, D.; Hinton, G.: "Erschreckend, wenn man das sieht" : KI-Pionier Geoffrey Hinton über KI-Modelle (2023) 0.02 
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    Date
    11. 5.2023 14:40:31
  3. Frobese, D.T.: Klassifikationsaufgaben mit der SENTRAX : Konkreter Fall: Automatische Detektion von SPAM (2006) 0.01 
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    Date
    26.12.2011 13:12:40
  4. Rötzer, F.: Kann KI mit KI generierte Texte erkennen? (2019) 0.01 
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    Abstract
    OpenAI hat einen Algorithmus zur Textgenerierung angeblich nicht vollständig veröffentlicht, weil er so gut sei und Missbrauch und Täuschung ermöglicht. Das u.a. von Elon Musk und Peter Thiel gegründete KI-Unternehmen OpenAI hatte im Februar erklärt, man habe den angeblich am weitesten fortgeschrittenen Algorithmus zur Sprachverarbeitung entwickelt. Der Algorithmus wurde lediglich anhand von 40 Gigabyte an Texten oder an 8 Millionen Webseiten trainiert, das nächste Wort in einem vorgegebenen Textausschnitt vorherzusagen. Damit könne man zusammenhängende, sinnvolle Texte erzeugen, die vielen Anforderungen genügen, zudem könne damit rudimentär Leseverständnis, Antworten auf Fragen, Zusammenfassungen und Übersetzungen erzeugt werden, ohne dies trainiert zu haben.
  5. Lezius, W.: Morphy - Morphologie und Tagging für das Deutsche (2013) 0.01 
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    Date
    22. 3.2015 9:30:24
  6. Bager, J.: ¬Die Text-KI ChatGPT schreibt Fachtexte, Prosa, Gedichte und Programmcode (2023) 0.01 
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    Date
    29.12.2022 18:22:55
  7. Rieger, F.: Lügende Computer (2023) 0.01 
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    Date
    16. 3.2023 19:22:55
  8. Altmann, E.G.; Cristadoro, G.; Esposti, M.D.: On the origin of long-range correlations in texts (2012) 0.01 
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    Date
    24. 7.2012 11:40:06
  9. Chowdhury, A.; Mccabe, M.C.: Improving information retrieval systems using part of speech tagging (1993) 0.01 
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    Abstract
    The object of Information Retrieval is to retrieve all relevant documents for a user query and only those relevant documents. Much research has focused on achieving this objective with little regard for storage overhead or performance. In the paper we evaluate the use of Part of Speech Tagging to improve, the index storage overhead and general speed of the system with only a minimal reduction to precision recall measurements. We tagged 500Mbs of the Los Angeles Times 1990 and 1989 document collection provided by TREC for parts of speech. We then experimented to find the most relevant part of speech to index. We show that 90% of precision recall is achieved with 40% of the document collections terms. We also show that this is a improvement in overhead with only a 1% reduction in precision recall.
  10. Lund, B.D.: ¬A brief review of ChatGPT : its value and the underlying GPT technology (2023) 0.01 
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    Date
    5. 1.2023 18:40:21
  11. Nagy T., I.: Detecting multiword expressions and named entities in natural language texts (2014) 0.01 
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    Date
    30.10.2014 18:40:33
  12. Jha, A.: Why GPT-4 isn't all it's cracked up to be (2023) 0.01 
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    Date
    24. 3.2023 14:40:45
  13. Rötzer, F.: KI-Programm besser als Menschen im Verständnis natürlicher Sprache (2018) 0.01 
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    Date
    22. 1.2018 11:32:44
  14. RWI/PH: Auf der Suche nach dem entscheidenden Wort : die Häufung bestimmter Wörter innerhalb eines Textes macht diese zu Schlüsselwörtern (2012) 0.00 
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    Date
    24. 7.2012 11:40:06

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