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  • × theme_ss:"Computerlinguistik"
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  1. Rindflesch, T.C.; Aronson, A.R.: Semantic processing in information retrieval (1993) 0.00 
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    Abstract
    Intuition suggests that one way to enhance the information retrieval process would be the use of phrases to characterize the contents of text. A number of researchers, however, have noted that phrases alone do not improve retrieval effectiveness. In this paper we briefly review the use of phrases in information retrieval and then suggest extensions to this paradigm using semantic information. We claim that semantic processing, which can be viewed as expressing relations between the concepts represented by phrases, will in fact enhance retrieval effectiveness. The availability of the UMLS® domain model, which we exploit extensively, significantly contributes to the feasibility of this processing.
    Type
    a
  2. Dias, G.: Multiword unit hybrid extraction (o.J.) 0.00 
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    Abstract
    This paper describes an original hybrid system that extracts multiword unit candidates from part-of-speech tagged corpora. While classical hybrid systems manually define local part-of-speech patterns that lead to the identification of well-known multiword units (mainly compound nouns), our solution automatically identifies relevant syntactical patterns from the corpus. Word statistics are then combined with the endogenously acquired linguistic information in order to extract the most relevant sequences of words. As a result, (1) human intervention is avoided providing total flexibility of use of the system and (2) different multiword units like phrasal verbs, adverbial locutions and prepositional locutions may be identified. The system has been tested on the Brown Corpus leading to encouraging results
    Type
    a
  3. Holland, M.: Erstes wissenschaftliches Buch eines Algorithmus' veröffentlicht (2019) 0.00 
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    Abstract
    Der Wissenschaftsverlag Springer Nature hat nach eigenen Angaben das erste Buch veröffentlicht, das von einem Algorithmus verfasst wurde. Bei Springer Nature ist das nach Angaben des Wissenschaftsverlags erste maschinengenerierte Buch erschienen: "Lithium-Ion Batteries - A Machine-Generated Summary of Current Research" biete einen Überblick über die neuesten Forschungspublikationen über Lithium-Ionen-Batterien, erklärte die Goethe-Universität Frankfurt am Main. Dort wurde im Bereich Angewandte Computerlinguistik unter der Leitung von Christian Chiarcos jenes Verfahren entwickelt, das Textinhalte automatisch analysiert und relevante Publikationen auswählen kann. Es heißt "Beta Writer" und steht als Autor über dem Buch.
    Type
    a
  4. Aizawa, A.; Kohlhase, M.: Mathematical information retrieval (2021) 0.00 
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  5. 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
  6. Baierer, K.; Zumstein, P.: Verbesserung der OCR in digitalen Sammlungen von Bibliotheken (2016) 0.00 
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  7. Kurz, C.: Womit sich Strafverfolger bald befassen müssen : ChatGPT (2023) 0.00 
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  8. Bischoff, M.: Wie eine KI lernt, sich selbst zu erklären (2023) 0.00 
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  9. Franke-Maier, M.: Computerlinguistik und Bibliotheken : Editorial (2016) 0.00 
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    Editor
    Ledl, A.
    Type
    a
  10. Rötzer, F.: Kann KI mit KI generierte Texte erkennen? (2019) 0.00 
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  11. Stieler, W.: Anzeichen von Bewusstsein bei ChatGPT und Co.? (2023) 0.00 
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  12. Szöke, D.: ChatGPT : wie Sie die KI ausprobieren können (2022) 0.00 
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  13. Räwel, J.: Automatisierte Kommunikation (2023) 0.00 
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  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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    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."
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  15. Menge-Sonnentag, R.: Google veröffentlicht einen Parser für natürliche Sprache (2016) 0.00 
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  16. Weßels, D.: ChatGPT - ein Meilenstein der KI-Entwicklung (2022) 0.00 
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