Search (575 results, page 1 of 29)

  • × theme_ss:"Computerlinguistik"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.19
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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
    Language
    e
  2. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.13
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    Language
    e
    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) 0.11
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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
    Language
    e
  4. Salton, G.; Buckley, C.; Smith, M.: On the application of syntactic methodologies in automatic text analysis (1990) 0.07
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    Language
    e
    Source
    Information processing and management. 26(1990) no.1, S.73-92
  5. Al-Shawakfa, E.; Al-Badarneh, A.; Shatnawi, S.; Al-Rabab'ah, K.; Bani-Ismail, B.: ¬A comparison study of some Arabic root finding algorithms (2010) 0.03
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    Abstract
    Arabic has a complex structure, which makes it difficult to apply natural language processing (NLP). Much research on Arabic NLP (ANLP) does exist; however, it is not as mature as that of other languages. Finding Arabic roots is an important step toward conducting effective research on most of ANLP applications. The authors have studied and compared six root-finding algorithms with success rates of over 90%. All algorithms of this study did not use the same testing corpus and/or benchmarking measures. They unified the testing process by implementing their own algorithm descriptions and building a corpus out of 3823 triliteral roots, applying 73 triliteral patterns, and with 18 affixes, producing around 27.6 million words. They tested the algorithms with the generated corpus and have obtained interesting results; they offer to share the corpus freely for benchmarking and ANLP research.
    Language
    e
  6. Ingenerf, J.: Disambiguating lexical meaning : conceptual meta-modelling as a means of controlling semantic language analysis (1994) 0.03
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    Language
    e
    Pages
    S.63-73
  7. Ponte, J.M.: Language models for relevance feedback (2000) 0.03
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    Language
    e
    Pages
    S.73-95
  8. Andrushchenko, M.; Sandberg, K.; Turunen, R.; Marjanen, J.; Hatavara, M.; Kurunmäki, J.; Nummenmaa, T.; Hyvärinen, M.; Teräs, K.; Peltonen, J.; Nummenmaa, J.: Using parsed and annotated corpora to analyze parliamentarians' talk in Finland (2022) 0.02
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    Language
    e
    Source
    Journal of the Association for Information Science and Technology. 73(2022) no.2, S.288-302
  9. Suissa, O.; Elmalech, A.; Zhitomirsky-Geffet, M.: Text analysis using deep neural networks in digital humanities and information science (2022) 0.02
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    Language
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    Source
    Journal of the Association for Information Science and Technology. 73(2022) no.2, S.268-287
  10. Warner, A.J.: Natural language processing (1987) 0.02
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    Language
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    Source
    Annual review of information science and technology. 22(1987), S.79-108
  11. McMahon, J.G.; Smith, F.J.: Improved statistical language model performance with automatic generated word hierarchies (1996) 0.02
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    Language
    e
    Source
    Computational linguistics. 22(1996) no.2, S.217-248
  12. 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
    Language
    e
  13. Somers, H.: Example-based machine translation : Review article (1999) 0.02
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    Date
    31. 7.1996 9:22:19
    Language
    e
  14. New tools for human translators (1997) 0.02
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    Date
    31. 7.1996 9:22:19
    Language
    e
  15. 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
    Language
    e
  16. 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) 0.01
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    Date
    15. 3.2000 10:22:37
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  17. Boleda, G.; Evert, S.: Multiword expressions : a pain in the neck of lexical semantics (2009) 0.01
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    Date
    1. 3.2013 14:56:22
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    e
  18. Hutchins, J.: From first conception to first demonstration : the nascent years of machine translation, 1947-1954. A chronology (1997) 0.01
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    Date
    31. 7.1996 9:22:19
    Language
    e
  19. Franke-Maier, M.: Computerlinguistik und Bibliotheken : Editorial (2016) 0.01
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    Abstract
    Vor 50 Jahren, im Februar 1966, wies Floyd M. Cammack auf den Zusammenhang von "Linguistics and Libraries" hin. Er ging dabei von dem Eintrag für "Linguistics" in den Library of Congress Subject Headings (LCSH) von 1957 aus, der als Verweis "See Language and Languages; Philology; Philology, Comparative" enthielt. Acht Jahre später kamen unter dem Schlagwort "Language and Languages" Ergänzungen wie "language data processing", "automatic indexing", "machine translation" und "psycholinguistics" hinzu. Für Cammack zeigt sich hier ein Netz komplexer Wechselbeziehungen, die unter dem Begriff "Linguistics" zusammengefasst werden sollten. Dieses System habe wichtigen Einfluss auf alle, die mit dem Sammeln, Organisieren, Speichern und Wiederauffinden von Informationen befasst seien. (Cammack 1966:73). Hier liegt - im übertragenen Sinne - ein Heft vor Ihnen, in dem es um computerlinguistische Verfahren in Bibliotheken geht. Letztlich geht es um eine Versachlichung der Diskussion, um den Stellenwert der Inhaltserschliessung und die Rekalibrierung ihrer Wertschätzung in Zeiten von Mega-Indizes und Big Data. Der derzeitige Widerspruch zwischen dem Wunsch nach relevanter Treffermenge in Rechercheoberflächen vs. der Erfahrung des Relevanz-Rankings ist zu lösen. Explizit auch die Frage, wie oft wir von letzterem enttäuscht wurden und was zu tun ist, um das Verhältnis von recall und precision wieder in ein angebrachtes Gleichgewicht zu bringen. Unsere Nutzerinnen und Nutzer werden es uns danken.
  20. Riloff, E.: ¬An empirical study of automated dictionary construction for information extraction in three domains (1996) 0.01
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    Date
    6. 3.1997 16:22:15
    Language
    e

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