Search (53 results, page 1 of 3)

  • × theme_ss:"Computerlinguistik"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.25 
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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
  2. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.19 
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  3. Huo, W.: Automatic multi-word term extraction and its application to Web-page summarization (2012) 0.16 
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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
  4. Rötzer, F.: Computer ergooglen die Bedeutung von Worten (2005) 0.01 
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    Content
    "Wie könnten Computer Sprache lernen und dabei auch die Bedeutung von Worten sowie die Beziehungen zwischen ihnen verstehen? Dieses Problem der Semantik stellt eine gewaltige, bislang nur ansatzweise bewältigte Aufgabe dar, da Worte und Wortverbindungen oft mehrere oder auch viele Bedeutungen haben, die zudem vom außersprachlichen Kontext abhängen. Die beiden holländischen (Ein künstliches Bewusstsein aus einfachen Aussagen (1)). Paul Vitanyi (2) und Rudi Cilibrasi vom Nationalen Institut für Mathematik und Informatik (3) in Amsterdam schlagen eine elegante Lösung vor: zum Nachschlagen im Internet, der größten Datenbank, die es gibt, wird einfach Google benutzt. Objekte wie eine Maus können mit ihren Namen "Maus" benannt werden, die Bedeutung allgemeiner Begriffe muss aus ihrem Kontext gelernt werden. Ein semantisches Web zur Repräsentation von Wissen besteht aus den möglichen Verbindungen, die Objekte und ihre Namen eingehen können. Natürlich können in der Wirklichkeit neue Namen, aber auch neue Bedeutungen und damit neue Verknüpfungen geschaffen werden. Sprache ist lebendig und flexibel. Um einer Künstlichen Intelligenz alle Wortbedeutungen beizubringen, müsste mit der Hilfe von menschlichen Experten oder auch vielen Mitarbeitern eine riesige Datenbank mit den möglichen semantischen Netzen aufgebaut und dazu noch ständig aktualisiert werden. Das aber müsste gar nicht notwendig sein, denn mit dem Web gibt es nicht nur die größte und weitgehend kostenlos benutzbare semantische Datenbank, sie wird auch ständig von zahllosen Internetnutzern aktualisiert. Zudem gibt es Suchmaschinen wie Google, die Verbindungen zwischen Worten und damit deren Bedeutungskontext in der Praxis in ihrer Wahrscheinlichkeit quantitativ mit der Angabe der Webseiten, auf denen sie gefunden wurden, messen.
  5. Warner, A.J.: Natural language processing (1987) 0.00 
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    Source
    Annual review of information science and technology. 22(1987), S.79-108
  6. McMahon, J.G.; Smith, F.J.: Improved statistical language model performance with automatic generated word hierarchies (1996) 0.00 
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    Source
    Computational linguistics. 22(1996) no.2, S.217-248
  7. Ruge, G.: ¬A spreading activation network for automatic generation of thesaurus relationships (1991) 0.00 
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    Date
    8.10.2000 11:52:22
  8. Somers, H.: Example-based machine translation : Review article (1999) 0.00 
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    Date
    31. 7.1996 9:22:19
  9. New tools for human translators (1997) 0.00 
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    Date
    31. 7.1996 9:22:19
  10. Baayen, R.H.; Lieber, H.: Word frequency distributions and lexical semantics (1997) 0.00 
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    Date
    28. 2.1999 10:48:22
  11. ¬Der Student aus dem Computer (2023) 0.00 
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    Date
    27. 1.2023 16:22:55
  12. Byrne, C.C.; McCracken, S.A.: ¬An adaptive thesaurus employing semantic distance, relational inheritance and nominal compound interpretation for linguistic support of information retrieval (1999) 0.00 
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    Date
    15. 3.2000 10:22:37
  13. Boleda, G.; Evert, S.: Multiword expressions : a pain in the neck of lexical semantics (2009) 0.00 
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    Date
    1. 3.2013 14:56:22
  14. Monnerjahn, P.: Vorsprung ohne Technik : Übersetzen: Computer und Qualität (2000) 0.00 
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    Source
    c't. 2000, H.22, S.230-231
  15. Hutchins, J.: From first conception to first demonstration : the nascent years of machine translation, 1947-1954. A chronology (1997) 0.00 
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    Date
    31. 7.1996 9:22:19
  16. Kuhlmann, U.; Monnerjahn, P.: Sprache auf Knopfdruck : Sieben automatische Übersetzungsprogramme im Test (2000) 0.00 
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    c't. 2000, H.22, S.220-229
  17. Lezius, W.; Rapp, R.; Wettler, M.: ¬A morphology-system and part-of-speech tagger for German (1996) 0.00 
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    Date
    22. 3.2015 9:37:18
  18. Wanner, L.: Lexical choice in text generation and machine translation (1996) 0.00 
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  19. Riloff, E.: ¬An empirical study of automated dictionary construction for information extraction in three domains (1996) 0.00 
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Authors

Years

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  • e 36
  • d 17

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  • a 40
  • el 6
  • m 5
  • s 3
  • p 2
  • x 2
  • d 1
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