Search (119 results, page 1 of 6)

  • × theme_ss:"Computerlinguistik"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.14 
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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. Winograd, T.: Understanding natural language (1972) 0.03 
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  3. Meng, K.; Ba, Z.; Ma, Y.; Li, G.: ¬A network coupling approach to detecting hierarchical linkages between science and technology (2024) 0.03 
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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.
  4. Ontologie und Axiomatik der Wissensbasis von LILOG (1992) 0.03 
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    Editor
    Klose, G., E. Lang u. T. Pirlein
  5. Winograd, T.: ¬Ein prozedurales Modell des Sprachverstehens (1977) 0.03 
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  6. Strzalkowski, T.: Reversible grammar in natural langugae processing (19??) 0.03 
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  7. Perez-Carballo, J.; Strzalkowski, T.: Natural language information retrieval : progress report (2000) 0.03 
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  8. Pirkola, A.; Hedlund, T.; Keskustalo, H.; Järvelin, K.: Dictionary-based cross-language information retrieval : problems, methods, and research findings (2001) 0.03 
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  9. Nakagawa, H.; Mori, T.: Automatic term recognition based an statistics of compound nouns and their components (2003) 0.03 
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  10. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.03 
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  11. Warner, A.J.: Natural language processing (1987) 0.02 
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    Source
    Annual review of information science and technology. 22(1987), S.79-108
  12. Graham, T.: ¬The free language approach to online catalogues : the user (1985) 0.02 
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  13. Samad, T.: ¬A natural language interface for computer aided design (19??) 0.02 
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  14. McMahon, J.G.; Smith, F.J.: Improved statistical language model performance with automatic generated word hierarchies (1996) 0.02 
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    Source
    Computational linguistics. 22(1996) no.2, S.217-248
  15. Ruge, G.: ¬A spreading activation network for automatic generation of thesaurus relationships (1991) 0.02 
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    Date
    8.10.2000 11:52:22
  16. Somers, H.: Example-based machine translation : Review article (1999) 0.02 
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    Date
    31. 7.1996 9:22:19
  17. New tools for human translators (1997) 0.02 
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    Date
    31. 7.1996 9:22:19
  18. Baayen, R.H.; Lieber, H.: Word frequency distributions and lexical semantics (1997) 0.02 
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    Date
    28. 2.1999 10:48:22
  19. ¬Der Student aus dem Computer (2023) 0.02 
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    Date
    27. 1.2023 16:22:55
  20. Korman, D.Z.; Mack, E.; Jett, J.; Renear, A.H.: Defining textual entailment (2018) 0.02 
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    Abstract
    Textual entailment is a relationship that obtains between fragments of text when one fragment in some sense implies the other fragment. The automation of textual entailment recognition supports a wide variety of text-based tasks, including information retrieval, information extraction, question answering, text summarization, and machine translation. Much ingenuity has been devoted to developing algorithms for identifying textual entailments, but relatively little to saying what textual entailment actually is. This article is a review of the logical and philosophical issues involved in providing an adequate definition of textual entailment. We show that many natural definitions of textual entailment are refuted by counterexamples, including the most widely cited definition of Dagan et al. We then articulate and defend the following revised definition: T textually entails H?=?df typically, a human reading T would be justified in inferring the proposition expressed by H from the proposition expressed by T. We also show that textual entailment is context-sensitive, nontransitive, and nonmonotonic.

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  • e 81
  • d 36
  • m 1
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  • a 92
  • el 15
  • m 14
  • s 9
  • x 3
  • d 2
  • p 2
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