Search (19 results, page 1 of 1)

  • × theme_ss:"Computerlinguistik"
  • × year_i:[2020 TO 2030}
  1. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.20 
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  2. Siepmann, D.: Auswirkungen von KI auf die Textproduktion in der Wissenschaft (2023) 0.02 
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    Forschung und Lehre [https://www.forschung-und-lehre.de/zeitfragen/welche-auswirkungen-kis-auf-die-textproduktion-in-der-wissenschaft-haben-5740]
  3. Weßels, D.: ChatGPT - ein Meilenstein der KI-Entwicklung (2023) 0.02 
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    Content
    Wiederabdruck des Beitrags aus: Forschung & Lehre vom 20.12.2022. Vgl.: DOI 10.1515/dmvm-2023-0007.
  4. Donath, A.: Nutzungsverbote für ChatGPT (2023) 0.01 
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    Content
    Milliardenbewertung für ChatGPT OpenAI, das Chatbot ChatGPT betreibt, befindet sich laut einem Bericht des Wall Street Journals in Gesprächen zu einem Aktienverkauf. Das WSJ meldete, der mögliche Verkauf der Aktien würde die Bewertung von OpenAI auf 29 Milliarden US-Dollar anheben. Sorgen auch in Brandenburg Der brandenburgische SPD-Abgeordnete Erik Stohn stellte mit Hilfe von ChatGPT eine Kleine Anfrage an den Brandenburger Landtag, in der er fragte, wie die Landesregierung sicherstelle, dass Studierende bei maschinell erstellten Texten gerecht beurteilt und benotet würden. Er fragte auch nach Maßnahmen, die ergriffen worden seien, um sicherzustellen, dass maschinell erstellte Texte nicht in betrügerischer Weise von Studierenden bei der Bewertung von Studienleistungen verwendet werden könnten.
    Der Autor meint dazu Es ist verständlich, dass sich Lehrer und Wissenschaftler Gedanken darüber machen, wie die Verwendung von künstlicher Intelligenz in der Bildung nicht zu negativen Effekten führt. Es ist wichtig, dass Schüler fair beurteilt werden und niemand Vorteile aus einem Betrug hat. Gleichzeitig ist es jedoch auch wichtig, dass Schüler und Wissenschaftler die Möglichkeit haben, Technologien und Tools zu nutzen, die ihnen helfen können, ihr volles Potential auszuschöpfen. Es wird interessant sein, zu sehen, welche Maßnahmen ergriffen werden, um sicherzustellen, dass die Verwendung von KI in der Bildung und Forschung fair und sicher ist."
  5. Siegel, M.: Maschinelle Übersetzung (2023) 0.01 
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    Abstract
    Die maschinelle Übersetzung von Sprache ist ein Menschheitstraum, an dem nunmehr schon fast hundert Jahre geforscht wird. Maschinelle Übersetzung ermöglicht im Idealfall den Erhalt der diversen Sprachkulturen bei der gleichzeitigen Möglichkeit einer interkulturellen Verständigung. Die Forschung zur maschinellen Übersetzung ist dabei von Brüchen geprägt.
  6. Geißler, S.: Natürliche Sprachverarbeitung und Künstliche Intelligenz : ein wachsender Markt mit vielen Chancen. Das Beispiel Kairntech (2020) 0.01 
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    Abstract
    Vor rund einem Jahr haben wir an dieser Stelle die aufregende Dynamik auf den Gebieten der Natürlichen Sprachverarbeitung (NLP) und der Künstlichen Intelligenz (KI) beschrieben: Seit einigen Jahren sorgen Fortschritte in den algorithmischen Grundlagen, in der einsetzbaren Rechenleistung sowie in der Verfügbarkeit von großen Datenmengen für immer leistungsfähigere Systeme. NLP-Anwendungen seien damit mehr denn je reif für den praktischen Einsatz, hatten wir argumentiert. Diese Entwicklung verfolgen wir bei Kairntech nicht allein als interessierte Beobachter, sondern sie stellt die Grundlage unserer Arbeit dar, mit der wir NLP- und KI-Ansätze zur Anwendung auf konkreten geschäftskritischen Prozessen entwickeln und einsetzen. Experten gehen auch für die kommenden Jahre von einem anhaltenden Wachstum des weltweiten Marktes für NLP aus: Mit einem durchschnittlichen Wachstum von über 20 Prozent pro Jahr werde der Markt bis 2025 auf geschätzte 6,24 Milliarden US-$ anwachsen. Im Bereich der Forschung ist das Wachstum sogar noch stürmischer: So ist die Zahl der Einreichungen zur ACL-Konferenz, dem vielleicht wichtigsten jährlichen Event in diesem Gebiet, von 2018 bis 2019 um ganze 75 Prozent angestiegen. Im vorliegenden Text wollen wir die Richtung, die wir bei Kairntech mit der Gründung vor einem Jahr eingeschlagen haben, beschreiben sowie von ersten Erfolgen auf diesem Weg berichten.
  7. Barthel, J.; Ciesielski, R.: Regeln zu ChatGPT an Unis oft unklar : KI in der Bildung (2023) 0.00 
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    Date
    29. 3.2023 13:23:26
    29. 3.2023 13:29:19
  8. ¬Der Student aus dem Computer (2023) 0.00 
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    Date
    27. 1.2023 16:22:55
  9. Müller, P.: Text-Automat mit Tücken (2023) 0.00 
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    Source
    Pirmasenser Zeitung. Nr. 29 vom 03.02.2023, S.2
  10. Bischoff, M.: Was steckt hinter ChatGTP & Co? (2023) 0.00 
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    Date
    12. 4.2023 20:29:54
  11. Morris, V.: Automated language identification of bibliographic resources (2020) 0.00 
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    Date
    2. 3.2020 19:04:22
  12. Bager, J.: ¬Die Text-KI ChatGPT schreibt Fachtexte, Prosa, Gedichte und Programmcode (2023) 0.00 
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    Date
    29.12.2022 18:22:55
  13. Rieger, F.: Lügende Computer (2023) 0.00 
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    Date
    16. 3.2023 19:22:55
  14. 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
  15. Albrecht, I.: GPT-3: die Zukunft studentischer Hausarbeiten oder eine Bedrohung der wissenschaftlichen Integrität? (2023) 0.00 
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    Date
    28. 1.2022 11:05:29
  16. Lund, B.D.; Wang, T.; Mannuru, N.R.; Nie, B.; Shimray, S.; Wang, Z.: ChatGPT and a new academic reality : artificial Intelligence-written research papers and the ethics of the large language models in scholarly publishing (2023) 0.00 
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    Date
    19. 4.2023 19:29:44
  17. Pepper, S.; Arnaud, P.J.L.: Absolutely PHAB : toward a general model of associative relations (2020) 0.00 
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    Abstract
    There have been many attempts at classifying the semantic modification relations (R) of N + N compounds but this work has not led to the acceptance of a definitive scheme, so that devising a reusable classification is a worthwhile aim. The scope of this undertaking is extended to other binominal lexemes, i.e. units that contain two thing-morphemes without explicitly stating R, like prepositional units, N + relational adjective units, etc. The 25-relation taxonomy of Bourque (2014) was tested against over 15,000 binominal lexemes from 106 languages and extended to a 29-relation scheme ("Bourque2") through the introduction of two new reversible relations. Bourque2 is then mapped onto Hatcher's (1960) four-relation scheme (extended by the addition of a fifth relation, similarity , as "Hatcher2"). This results in a two-tier system usable at different degrees of granularities. On account of its semantic proximity to compounding, metonymy is then taken into account, following Janda's (2011) suggestion that it plays a role in word formation; Peirsman and Geeraerts' (2006) inventory of 23 metonymic patterns is mapped onto Bourque2, confirming the identity of metonymic and binominal modification relations. Finally, Blank's (2003) and Koch's (2001) work on lexical semantics justifies the addition to the scheme of a third, superordinate level which comprises the three Aristotelean principles of similarity, contiguity and contrast.
  18. Laparra, E.; Binford-Walsh, A.; Emerson, K.; Miller, M.L.; López-Hoffman, L.; Currim, F.; Bethard, S.: Addressing structural hurdles for metadata extraction from environmental impact statements (2023) 0.00 
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    Date
    29. 8.2023 19:21:01
  19. 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) 0.00 
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    Date
    23.11.2023 19:07:22

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