Search (49 results, page 1 of 3)

  • × year_i:[2010 TO 2020}
  • × theme_ss:"Computerlinguistik"
  1. Colace, F.; Santo, M. De; Greco, L.; Napoletano, P.: Weighted word pairs for query expansion (2015) 0.04 
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  2. Clark, M.; Kim, Y.; Kruschwitz, U.; Song, D.; Albakour, D.; Dignum, S.; Beresi, U.C.; Fasli, M.; Roeck, A De: Automatically structuring domain knowledge from text : an overview of current research (2012) 0.04 
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  3. Terminologie : Epochen - Schwerpunkte - Umsetzungen : zum 25-jährigen Bestehen des Rats für Deutschsprachige Terminologie (2019) 0.03 
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    Footnote
    Vgl.: https://www.springer.com/de/book/9783662589489.
    Type
    m
  4. Schmolz, H.: Anaphora resolution and text retrieval : a lnguistic analysis of hypertexts (2015) 0.02 
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    Imprint
    Berlin : De Gruyter Mouton
  5. Budin, G.: Zum Entwicklungsstand der Terminologiewissenschaft (2019) 0.01 
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    Footnote
    Vgl.: https://www.springer.com/de/book/9783662589489.
  6. Endres-Niggemeyer, B.: Thinkie: Lautes Denken mit Spracherkennung (mobil) (2013) 0.01 
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    Abstract
    Lautes Denken ist eine bewährte Methode zum Erforschen kognitiver Prozesse. Es wird in vielen Disziplinen benutzt, z. B. um aufzudecken, welche Erfahrungen Benutzer bei der Interaktion mit Computerschnittstellen machen. Nach einer kurzen Erklärung des Lauten Denkens wird die App Thinkie vorgestellt. Thinkie ist eine mobile Lösung für das Laute Denken auf iPhone und iPad. Die Testperson nimmt auf dem iPhone den Ton auf. Die Spracherkennungssoftware Siri (http://www.apple.com/de/ios/siri/) transkribiert ihn. Parallel wird auf dem iPad oder einem anderen Gerät gefilmt. Auf dem iPad kann man - mit Video im Blick - das Transkript aufarbeiten und interpretieren. Die Textdateien transportiert Thinkie über eine Cloud-Kollektion, die Filme werden mit iTunes übertragen. Thinkie ist noch nicht tauglich für den praktischen Gebrauch. Noch sind die Sequenzen zu kurz, die Siri verarbeiten kann. Das wird sich ändern.
  7. Mengel, T.: Wie viel Terminologiearbeit steckt in der Übersetzung der Dewey-Dezimalklassifikation? (2019) 0.01 
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    Footnote
    Vgl.: https://www.springer.com/de/book/9783662589489.
  8. Farreús, M.; Costa-jussà, M.R.; Popovic' Morse, M.: Study and correlation analysis of linguistic, perceptual, and automatic machine translation evaluations (2012) 0.01 
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  9. AL-Smadi, M.; Jaradat, Z.; AL-Ayyoub, M.; Jararweh, Y.: Paraphrase identification and semantic text similarity analysis in Arabic news tweets using lexical, syntactic, and semantic features (2017) 0.01 
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  10. Lezius, W.: Morphy - Morphologie und Tagging für das Deutsche (2013) 0.01 
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    Date
    22. 3.2015 9:30:24
  11. Carrillo-de-Albornoz, J.; Plaza, L.: ¬An emotion-based model of negation, intensifiers, and modality for polarity and intensity classification (2013) 0.01 
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  12. Collovini de Abreu, S.; Vieira, R.: RelP: Portuguese open relation extraction (2017) 0.01 
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  13. 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.
  14. Muneer, I.; Sharjeel, M.; Iqbal, M.; Adeel Nawab, R.M.; Rayson, P.: CLEU - A Cross-language english-urdu corpus and benchmark for text reuse experiments (2019) 0.01 
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  15. Wong, W.; Liu, W.; Bennamoun, M.: Ontology learning from text : a look back and into the future (2010) 0.01 
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  16. Strube, M.: Kreativ durch Analogien (2011) 0.01 
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  17. Shen, M.; Liu, D.-R.; Huang, Y.-S.: Extracting semantic relations to enrich domain ontologies (2012) 0.01 
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  18. Grigonyte, G.: Building and evaluating domain ontologies : NLP contributions (2010) 0.01 
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    Type
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  19. Holland, M.: Erstes wissenschaftliches Buch eines Algorithmus' veröffentlicht (2019) 0.01 
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  20. Liu, P.J.; Saleh, M.; Pot, E.; Goodrich, B.; Sepassi, R.; Kaiser, L.; Shazeer, N.: Generating Wikipedia by summarizing long sequences (2018) 0.01 
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