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  • × theme_ss:"Computerlinguistik"
  1. Ruge, G.: ¬A spreading activation network for automatic generation of thesaurus relationships (1991) 0.04 
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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
  2. Baayen, R.H.; Lieber, H.: Word frequency distributions and lexical semantics (1997) 0.04 
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    Date
    28. 2.1999 10:48:22
  3. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.04 
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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
  4. Doszkocs, T.E.; Zamora, A.: Dictionary services and spelling aids for Web searching (2004) 0.04 
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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) 0.04 
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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) 0.03 
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    Date
    29. 3.1996 18:28:09
  7. Heyer, G.; Quasthoff, U.; Wittig, T.: Text Mining : Wissensrohstoff Text. Konzepte, Algorithmen, Ergebnisse (2006) 0.03 
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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
  8. Gomez, F.: Learning word syntactic subcategorizations interactively (1995) 0.02 
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    Date
    29. 1.1996 18:28:58
  9. Chen, K.-H.: Evaluating Chinese text retrieval with multilingual queries (2002) 0.02 
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    Date
    6. 1.1997 18:30:28
    Source
    Knowledge organization. 29(2002) nos.3/4, S.156-170
  10. 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
    Source
    Knowledge organization. 29(2002) nos.3/4, S.198-203
  11. Rindflesch, T.C.; Aronson, A.R.: Semantic processing in information retrieval (1993) 0.02 
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    Date
    29. 6.2015 14:51:28
  12. Schneider, R.: Web 3.0 ante portas? : Integration von Social Web und Semantic Web (2008) 0.02 
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    Date
    22. 1.2011 10:38:28
  13. Ferret, O.; Grau, B.; Hurault-Plantet, M.; Illouz, G.; Jacquemin, C.; Monceaux, L.; Robba, I.; Vilnat, A.: How NLP can improve question answering (2002) 0.02 
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    Date
    6. 1.1997 18:30:28
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    Knowledge organization. 29(2002) nos.3/4, S.135-155
  14. Sidhom, S.; Hassoun, M.: Morpho-syntactic parsing for a text mining environment : An NP recognition model for knowledge visualization and information retrieval (2002) 0.02 
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    6. 1.1997 18:30:28
    Source
    Knowledge organization. 29(2002) nos.3/4, S.171-180
  15. L'Homme, D.; L'Homme, M.-C.; Lemay, C.: Benchmarking the performance of two Part-of-Speech (POS) taggers for terminological purposes (2002) 0.02 
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    6. 1.1997 18:30:28
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    Knowledge organization. 29(2002) nos.3/4, S.204-216
  16. Albrecht, I.: GPT-3: die Zukunft studentischer Hausarbeiten oder eine Bedrohung der wissenschaftlichen Integrität? (2023) 0.02 
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    Date
    28. 1.2022 11:05:29
  17. Strube, M.: Kreativ durch Analogien (2011) 0.02 
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    Content
    "Die Computerlinguistik vereinigt Elemente von Informatik und Linguistik; sie verwendet darüber hinaus Methoden aus weiteren Gebieten wie Mathematik, Psychologie, Statistik und künstliche Intelligenz. Der Reiz und die Herausforderung einer solchen interdisziplinären Wissenschaft liegen darin, Analogien zwischen Konzepten aus weit entfernten Teilgebieten zu erkennen und zu nutzen. Paradebeispiel dafür ist einer der entscheidenden Durchbrüche, welche die Computerlinguistik prägten. Es geht um das »Parsing«: Ein Computerprogramm, genauer gesagt ein Compiler, nimmt Zeichen für Zeichen den Input des Benutzers entgegen, der in diesem Fall seinerseits aus dem Text eines Computerprogramms besteht, und ermittelt dessen Struktur. Im Prinzip dasselbe tut ein Mensch, der einen gesprochenen Satz hört und versteht."
  18. Sünkler, S.; Kerkmann, F.; Schultheiß, S.: Ok Google . the end of search as we know it : sprachgesteuerte Websuche im Test (2018) 0.02 
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    Abstract
    Sprachsteuerungssysteme, die den Nutzer auf Zuruf unterstützen, werden im Zuge der Verbreitung von Smartphones und Lautsprechersystemen wie Amazon Echo oder Google Home zunehmend populär. Eine der zentralen Anwendungen dabei stellt die Suche in Websuchmaschinen dar. Wie aber funktioniert "googlen", wenn der Nutzer seine Suchanfrage nicht schreibt, sondern spricht? Dieser Frage ist ein Projektteam der HAW Hamburg nachgegangen und hat im Auftrag der Deutschen Telekom untersucht, wie effektiv, effizient und zufriedenstellend Google Now, Apple Siri, Microsoft Cortana sowie das Amazon Fire OS arbeiten. Ermittelt wurden Stärken und Schwächen der Systeme sowie Erfolgskriterien für eine hohe Gebrauchstauglichkeit. Diese Erkenntnisse mündeten in dem Prototyp einer optimalen Voice Web Search.
  19. Melby, A.: Some notes on 'The proper place of men and machines in language translation' (1997) 0.02 
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    Date
    31. 7.1996 9:22:19
    Source
    Machine translation. 12(1997) nos.1/2, S.29-34
  20. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.02 
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN

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