-
Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004)
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- Content
- Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
- Date
- 8. 1.2013 10:22:32
-
Hutchins, W.J.; Somers, H.L.: ¬An introduction to machine translation (1992)
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- Classification
- ST 306 Informatik / Monographien / Künstliche Intelligenz / Natürliche Sprachverarbeitung
Mat T 1091 / Automatische Übersetzung
- RVK
- ST 306 Informatik / Monographien / Künstliche Intelligenz / Natürliche Sprachverarbeitung
- SBB
- Mat T 1091 / Automatische Übersetzung
-
Sagawe, H.: Einfluß 'intelligenter' Maschinen auf menschliches Verhalten (1994)
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- Classification
- Mat T 1091 / Interaktives System
- SBB
- Mat T 1091 / Interaktives System
-
Noever, D.; Ciolino, M.: ¬The Turing deception (2022)
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- Source
- https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
-
Huo, W.: Automatic multi-word term extraction and its application to Web-page summarization (2012)
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- Content
- A Thesis presented to The University of Guelph In partial fulfilment of requirements for the degree of Master of Science in Computer Science. Vgl. Unter: http://www.inf.ufrgs.br%2F~ceramisch%2Fdownload_files%2Fpublications%2F2009%2Fp01.pdf.
- Date
- 10. 1.2013 19:22:47
-
Winograd, T.: ¬Ein prozedurales Modell des Sprachverstehens (1977)
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- Source
- Semantik und künstliche Intelligenz: Beiträge zur automatischen Sprachbearbeitung II. Hrsg. u. eingeleitet von P. Eisenberg
-
Leighton, T.: ChatGPT und Künstliche Intelligenz : Utopie oder Dystopie? (2023)
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- Source
- https://www.heise.de/tp/features/ChatGPT-und-Kuenstliche-Intelligenz-Utopie-oder-Dystopie-7445181.html?view=print
-
Schönbächler, E.; Strasser, T.; Himpsl-Gutermann, K.: Vom Chat zum Check : Informationskompetenz mit ChatGPT steigern (2023)
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- Abstract
- Der Beitrag greift den aktuellen Diskurs um die KI-Anwendung ChatGPT und deren Bedeutung in Schule und Hochschule auf. Dabei werden durch einen Überblick über verschiedene Assistenzsysteme, die auf Künstlicher Intelligenz beruhen, Grundlagen und Unterschiede herausgearbeitet. Der Bereich der Chatbots wird näher beleuchtet, die beiden grundlegenden Arten des regelbasierten Chatbots und des Machine Learning Bots werden anhand von anschaulichen Beispielen praxisnah erklärt. Schließlich wird herausgearbeitet, dass Informationskompetenz als Schlüsselkompetenz des 21. Jahrhunderts auch die wesentliche Grundlage dafür ist, im Bildungsbereich konstruktiv mit KI-Systemen wie ChatGPT umzugehen und die wesentlichen Funktionsmechanismen zu verstehen. Ein Unterrichtsentwurf zum Thema "Biene" schließt den Praxisbeitrag ab.
-
¬Der Student aus dem Computer (2023)
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- Abstract
- Kann gut zitieren, aber schlecht rechnen: Künstliche Intelligenz besteht Juraprüfung an US-Uni
- Date
- 27. 1.2023 16:22:55
-
Meng, K.; Ba, Z.; Ma, Y.; Li, G.: ¬A network coupling approach to detecting hierarchical linkages between science and technology (2024)
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- Abstract
- Detecting science-technology hierarchical linkages is beneficial for understanding deep interactions between science and technology (S&T). Previous studies have mainly focused on linear linkages between S&T but ignored their structural linkages. In this paper, we propose a network coupling approach to inspect hierarchical interactions of S&T by integrating their knowledge linkages and structural linkages. S&T knowledge networks are first enhanced with bidirectional encoder representation from transformers (BERT) knowledge alignment, and then their hierarchical structures are identified based on K-core decomposition. Hierarchical coupling preferences and strengths of the S&T networks over time are further calculated based on similarities of coupling nodes' degree distribution and similarities of coupling edges' weight distribution. Extensive experimental results indicate that our approach is feasible and robust in identifying the coupling hierarchy with superior performance compared to other isomorphism and dissimilarity algorithms. Our research extends the mindset of S&T linkage measurement by identifying patterns and paths of the interaction of S&T hierarchical knowledge.
-
Pirkola, A.; Hedlund, T.; Keskustalo, H.; Järvelin, K.: Dictionary-based cross-language information retrieval : problems, methods, and research findings (2001)
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-
Giesselbach, S.; Estler-Ziegler, T.: Dokumente schneller analysieren mit Künstlicher Intelligenz (2021)
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- Abstract
- Künstliche Intelligenz (KI) und natürliches Sprachverstehen (natural language understanding/NLU) verändern viele Aspekte unseres Alltags und unserer Arbeitsweise. Besondere Prominenz erlangte NLU durch Sprachassistenten wie Siri, Alexa und Google Now. NLU bietet Firmen und Einrichtungen das Potential, Prozesse effizienter zu gestalten und Mehrwert aus textuellen Inhalten zu schöpfen. So sind NLU-Lösungen in der Lage, komplexe, unstrukturierte Dokumente inhaltlich zu erschließen. Für die semantische Textanalyse hat das NLU-Team des IAIS Sprachmodelle entwickelt, die mit Deep-Learning-Verfahren trainiert werden. Die NLU-Suite analysiert Dokumente, extrahiert Eckdaten und erstellt bei Bedarf sogar eine strukturierte Zusammenfassung. Mit diesen Ergebnissen, aber auch über den Inhalt der Dokumente selbst, lassen sich Dokumente vergleichen oder Texte mit ähnlichen Informationen finden. KI-basierten Sprachmodelle sind der klassischen Verschlagwortung deutlich überlegen. Denn sie finden nicht nur Texte mit vordefinierten Schlagwörtern, sondern suchen intelligent nach Begriffen, die in ähnlichem Zusammenhang auftauchen oder als Synonym gebraucht werden. Der Vortrag liefert eine Einordnung der Begriffe "Künstliche Intelligenz" und "Natural Language Understanding" und zeigt Möglichkeiten, Grenzen, aktuelle Forschungsrichtungen und Methoden auf. Anhand von Praxisbeispielen wird anschließend demonstriert, wie NLU zur automatisierten Belegverarbeitung, zur Katalogisierung von großen Datenbeständen wie Nachrichten und Patenten und zur automatisierten thematischen Gruppierung von Social Media Beiträgen und Publikationen genutzt werden kann.
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Hartnett, K.: Sind Sprachmodelle bald die besseren Mathematiker? (2023)
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- Series
- Künstliche Intelligenz
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Strzalkowski, T.: Robust text processing in automated information retrieval (1994)
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- Footnote
- Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.317-322.
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Janssen, J.-K.: ChatGPT-Klon läuft lokal auf jedem Rechner : Alpaca/LLaMA ausprobiert (2023)
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- Source
- https://www.heise.de/news/c-t-3003-ChatGPT-Klon-laeuft-lokal-auf-jedem-Rechner-Alpaca-LLaMA-ausprobiert-8004159.html?view=print
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Al-Khatib, K.; Ghosa, T.; Hou, Y.; Waard, A. de; Freitag, D.: Argument mining for scholarly document processing : taking stock and looking ahead (2021)
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- Footnote
- Rez in: Künstliche Intelligenz. 1994, H.2, S.75-76 (A. Rothkegel)
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- Künstliche Intelligenz. 1995, H.5, S.27-33
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Winograd, T.: Understanding natural language (1972)
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