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  • × theme_ss:"Computerlinguistik"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.03
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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. Ruge, G.: ¬A spreading activation network for automatic generation of thesaurus relationships (1991) 0.03
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    Date
    8.10.2000 11:52:22
    Source
    Library science with a slant to documentation. 28(1991) no.4, S.125-130
  3. Baayen, R.H.; Lieber, H.: Word frequency distributions and lexical semantics (1997) 0.03
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    Date
    28. 2.1999 10:48:22
  4. Doszkocs, T.E.; Zamora, A.: Dictionary services and spelling aids for Web searching (2004) 0.03
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    Date
    14. 8.2004 17:22:56
    Source
    Online. 28(2004) no.3, S.22-29
  5. Mustafa El Hadi, W.: Evaluating human language technology : general applications to information access and management (2002) 0.03
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    Date
    6. 1.1997 18:30:28
    Source
    Knowledge organization. 29(2002) nos.3/4, S.124-134
  6. Whitelock, P.; Kilby, K.: Linguistic and computational techniques in machine translation system design : 2nd ed (1995) 0.02
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    Date
    29. 3.1996 18:28:09
  7. Abdelali, A.: Localization in modern standard Arabic (2004) 0.02
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    Abstract
    Modern Standard Arabic (MSA) is the official language used in all Arabic countries. In this paper we describe an investigation of the uniformity of MSA across different countries. Many studies have been carried out locally or regionally an Arabic and its dialects. Here we look an a more global scale by studying language variations between countries. The source material used in this investigation was derived from national newspapers available an the Web, which provided samples of common media usage in each country. This corpus has been used to investigate the lexical characteristics of Modern Standard Arabic as found in 10 different Arabic speaking countries. We describe our collection methods, the types of lexical analysis performed, and the results of our investigations. With respect to newspaper articles, MSA seems to be very uniform across all the countries included in the study, but we have detected various types of differences, with implications for computational processing of MSA.
    Source
    Journal of the American Society for Information Science and technology. 55(2004) no.1, S.23-28
  8. Heyer, G.; Quasthoff, U.; Wittig, T.: Text Mining : Wissensrohstoff Text. Konzepte, Algorithmen, Ergebnisse (2006) 0.02
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    Abstract
    Ein großer Teil des Weltwissens befindet sich in Form digitaler Texte im Internet oder in Intranets. Heutige Suchmaschinen nutzen diesen Wissensrohstoff nur rudimentär: Sie können semantische Zusammen-hänge nur bedingt erkennen. Alle warten auf das semantische Web, in dem die Ersteller von Text selbst die Semantik einfügen. Das wird aber noch lange dauern. Es gibt jedoch eine Technologie, die es bereits heute ermöglicht semantische Zusammenhänge in Rohtexten zu analysieren und aufzubereiten. Das Forschungsgebiet "Text Mining" ermöglicht es mit Hilfe statistischer und musterbasierter Verfahren, Wissen aus Texten zu extrahieren, zu verarbeiten und zu nutzen. Hier wird die Basis für die Suchmaschinen der Zukunft gelegt. Das erste deutsche Lehrbuch zu einer bahnbrechenden Technologie: Text Mining: Wissensrohstoff Text Konzepte, Algorithmen, Ergebnisse Ein großer Teil des Weltwissens befindet sich in Form digitaler Texte im Internet oder in Intranets. Heutige Suchmaschinen nutzen diesen Wissensrohstoff nur rudimentär: Sie können semantische Zusammen-hänge nur bedingt erkennen. Alle warten auf das semantische Web, in dem die Ersteller von Text selbst die Semantik einfügen. Das wird aber noch lange dauern. Es gibt jedoch eine Technologie, die es bereits heute ermöglicht semantische Zusammenhänge in Rohtexten zu analysieren und aufzubereiten. Das For-schungsgebiet "Text Mining" ermöglicht es mit Hilfe statistischer und musterbasierter Verfahren, Wissen aus Texten zu extrahieren, zu verarbeiten und zu nutzen. Hier wird die Basis für die Suchmaschinen der Zukunft gelegt. Was fällt Ihnen bei dem Wort "Stich" ein? Die einen denken an Tennis, die anderen an Skat. Die verschiedenen Zusammenhänge können durch Text Mining automatisch ermittelt und in Form von Wortnetzen dargestellt werden. Welche Begriffe stehen am häufigsten links und rechts vom Wort "Festplatte"? Welche Wortformen und Eigennamen treten seit 2001 neu in der deutschen Sprache auf? Text Mining beantwortet diese und viele weitere Fragen. Tauchen Sie mit diesem Lehrbuch ein in eine neue, faszinierende Wissenschaftsdisziplin und entdecken Sie neue, bisher unbekannte Zusammenhänge und Sichtweisen. Sehen Sie, wie aus dem Wissensrohstoff Text Wissen wird! Dieses Lehrbuch richtet sich sowohl an Studierende als auch an Praktiker mit einem fachlichen Schwerpunkt in der Informatik, Wirtschaftsinformatik und/oder Linguistik, die sich über die Grundlagen, Verfahren und Anwendungen des Text Mining informieren möchten und Anregungen für die Implementierung eigener Anwendungen suchen. Es basiert auf Arbeiten, die während der letzten Jahre an der Abteilung Automatische Sprachverarbeitung am Institut für Informatik der Universität Leipzig unter Leitung von Prof. Dr. Heyer entstanden sind. Eine Fülle praktischer Beispiele von Text Mining-Konzepten und -Algorithmen verhelfen dem Leser zu einem umfassenden, aber auch detaillierten Verständnis der Grundlagen und Anwendungen des Text Mining. Folgende Themen werden behandelt: Wissen und Text Grundlagen der Bedeutungsanalyse Textdatenbanken Sprachstatistik Clustering Musteranalyse Hybride Verfahren Beispielanwendungen Anhänge: Statistik und linguistische Grundlagen 360 Seiten, 54 Abb., 58 Tabellen und 95 Glossarbegriffe Mit kostenlosen e-learning-Kurs "Schnelleinstieg: Sprachstatistik" Zusätzlich zum Buch gibt es in Kürze einen Online-Zertifikats-Kurs mit Mentor- und Tutorunterstützung.
    Date
    19. 7.2006 20:28:27
  9. Schmidt, R.: Maschinelle Text-Ton-Synchronisation in Wissenschaft und Wirtschaft (2000) 0.02
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    Date
    26.12.2000 13:28:56
    Source
    Sprachtechnologie für eine dynamische Wirtschaft im Medienzeitalter - Language technologies for dynamic business in the age of the media - L'ingénierie linguistique au service de la dynamisation économique à l'ère du multimédia: Tagungsakten der XXVI. Jahrestagung der Internationalen Vereinigung Sprache und Wirtschaft e.V., 23.-25.11.2000, Fachhochschule Köln. Hrsg.: K.-D. Schmitz
  10. Sienel, J.; Weiss, M.; Laube, M.: Sprachtechnologien für die Informationsgesellschaft des 21. Jahrhunderts (2000) 0.02
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    Date
    26.12.2000 13:22:17
    Source
    Sprachtechnologie für eine dynamische Wirtschaft im Medienzeitalter - Language technologies for dynamic business in the age of the media - L'ingénierie linguistique au service de la dynamisation économique à l'ère du multimédia: Tagungsakten der XXVI. Jahrestagung der Internationalen Vereinigung Sprache und Wirtschaft e.V., 23.-25.11.2000, Fachhochschule Köln. Hrsg.: K.-D. Schmitz
  11. Gomez, F.: Learning word syntactic subcategorizations interactively (1995) 0.02
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  12. Chen, K.-H.: Evaluating Chinese text retrieval with multilingual queries (2002) 0.02
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    Date
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    Knowledge organization. 29(2002) nos.3/4, S.156-170
  13. Bowker, L.: Information retrieval in translation memory systems : assessment of current limitations and possibilities for future development (2002) 0.02
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    Date
    6. 1.1997 18:30:28
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    Knowledge organization. 29(2002) nos.3/4, S.198-203
  14. Rindflesch, T.C.; Aronson, A.R.: Semantic processing in information retrieval (1993) 0.02
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    29. 6.2015 14:51:28
  15. Schneider, R.: Web 3.0 ante portas? : Integration von Social Web und Semantic Web (2008) 0.02
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    22. 1.2011 10:38:28
  16. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.01
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  17. Ferret, O.; Grau, B.; Hurault-Plantet, M.; Illouz, G.; Jacquemin, C.; Monceaux, L.; Robba, I.; Vilnat, A.: How NLP can improve question answering (2002) 0.01
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    Knowledge organization. 29(2002) nos.3/4, S.135-155
  18. Sidhom, S.; Hassoun, M.: Morpho-syntactic parsing for a text mining environment : An NP recognition model for knowledge visualization and information retrieval (2002) 0.01
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    Date
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  19. L'Homme, D.; L'Homme, M.-C.; Lemay, C.: Benchmarking the performance of two Part-of-Speech (POS) taggers for terminological purposes (2002) 0.01
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    Date
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  20. Albrecht, I.: GPT-3: die Zukunft studentischer Hausarbeiten oder eine Bedrohung der wissenschaftlichen Integrität? (2023) 0.01
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    Date
    28. 1.2022 11:05:29

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