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  • × theme_ss:"Computerlinguistik"
  • × type_ss:"el"
  1. Zhai, X.: ChatGPT user experience: : implications for education (2022) 0.00 
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    Abstract
    ChatGPT, a general-purpose conversation chatbot released on November 30, 2022, by OpenAI, is expected to impact every aspect of society. However, the potential impacts of this NLP tool on education remain unknown. Such impact can be enormous as the capacity of ChatGPT may drive changes to educational learning goals, learning activities, and assessment and evaluation practices. This study was conducted by piloting ChatGPT to write an academic paper, titled Artificial Intelligence for Education (see Appendix A). The piloting result suggests that ChatGPT is able to help researchers write a paper that is coherent, (partially) accurate, informative, and systematic. The writing is extremely efficient (2-3 hours) and involves very limited professional knowledge from the author. Drawing upon the user experience, I reflect on the potential impacts of ChatGPT, as well as similar AI tools, on education. The paper concludes by suggesting adjusting learning goals-students should be able to use AI tools to conduct subject-domain tasks and education should focus on improving students' creativity and critical thinking rather than general skills. To accomplish the learning goals, researchers should design AI-involved learning tasks to engage students in solving real-world problems. ChatGPT also raises concerns that students may outsource assessment tasks. This paper concludes that new formats of assessments are needed to focus on creativity and critical thinking that AI cannot substitute.
  2. Shree, P.: ¬The journey of Open AI GPT models (2020) 0.00 
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    Abstract
    Generative Pre-trained Transformer (GPT) models by OpenAI have taken natural language processing (NLP) community by storm by introducing very powerful language models. These models can perform various NLP tasks like question answering, textual entailment, text summarisation etc. without any supervised training. These language models need very few to no examples to understand the tasks and perform equivalent or even better than the state-of-the-art models trained in supervised fashion. In this article we will cover the journey of these models and understand how they have evolved over a period of 2 years. 1. Discussion of GPT-1 paper (Improving Language Understanding by Generative Pre-training). 2. Discussion of GPT-2 paper (Language Models are unsupervised multitask learners) and its subsequent improvements over GPT-1. 3. Discussion of GPT-3 paper (Language models are few shot learners) and the improvements which have made it one of the most powerful models NLP has seen till date. This article assumes familiarity with the basics of NLP terminologies and transformer architecture.
    Type
    a
  3. Collins, C.: WordNet explorer : applying visualization principles to lexical semantics (2006) 0.00 
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    Abstract
    Interface designs for lexical databases in NLP have suffered from not following design principles developed in the information visualization research community. We present a design paradigm and show it can be used to generate visualizations which maximize the usability and utility ofWordNet. The techniques can be generally applied to other lexical databases used in NLP research.
  4. Baierer, K.; Zumstein, P.: Verbesserung der OCR in digitalen Sammlungen von Bibliotheken (2016) 0.00 
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  5. Kurz, C.: Womit sich Strafverfolger bald befassen müssen : ChatGPT (2023) 0.00 
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  6. Bischoff, M.: Wie eine KI lernt, sich selbst zu erklären (2023) 0.00 
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  7. Franke-Maier, M.: Computerlinguistik und Bibliotheken : Editorial (2016) 0.00 
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    Editor
    Ledl, A.
    Type
    a
  8. Rötzer, F.: Kann KI mit KI generierte Texte erkennen? (2019) 0.00 
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  9. Stieler, W.: Anzeichen von Bewusstsein bei ChatGPT und Co.? (2023) 0.00 
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  10. Szöke, D.: ChatGPT : wie Sie die KI ausprobieren können (2022) 0.00 
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  11. Räwel, J.: Automatisierte Kommunikation (2023) 0.00 
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  12. 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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    Content
    Die statistische Textanalyse funktioniert unabhängig von der Sprache Während sowohl Buchstaben als auch Wörter Langzeit-korreliert sind, kommen Buchstaben nur selten an bestimmten Stellen eines Textes gehäuft vor. "Ein Buchstabe ist eben nur sehr selten so eng mit einem Thema verknüpft wie das Wort zu dem er einen Teil beiträgt. Buchstaben sind sozusagen flexibler einsetzbar", sagt Altmann. Ein "a" beispielsweise kann zu einer ganzen Reihe von Wörtern beitragen, die nicht mit demselben Thema in Verbindung stehen. Mit Hilfe der statistischen Analyse von Texten ist es den Forschern gelungen, die prägenden Wörter eines Textes auf einfache Weise zu ermitteln. "Dabei ist es vollkommen egal, in welcher Sprache ein Text geschrieben ist. Es geht nur noch um die Geschichte und nicht um sprachspezifische Regeln", sagt Altmann. Die Ergebnisse könnten zukünftig zur Verbesserung von Internetsuchmaschinen beitragen, aber auch bei Textanalysen und der Suche nach Plagiaten helfen."
    Type
    a
  13. Menge-Sonnentag, R.: Google veröffentlicht einen Parser für natürliche Sprache (2016) 0.00 
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  14. Donath, A.: Nutzungsverbote für ChatGPT (2023) 0.00 
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  15. Weßels, D.: ChatGPT - ein Meilenstein der KI-Entwicklung (2022) 0.00 
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