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  1. Kleineberg, M.: Context analysis and context indexing : formal pragmatics in knowledge organization (2014) 0.32 
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    Source
    http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&ved=0CDQQFjAE&url=http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F3131107&ei=HzFWVYvGMsiNsgGTyoFI&usg=AFQjCNE2FHUeR9oQTQlNC4TPedv4Mo3DaQ&sig2=Rlzpr7a3BLZZkqZCXXN_IA&bvm=bv.93564037,d.bGg&cad=rja
  2. Mas, S.; Marleau, Y.: Proposition of a faceted classification model to support corporate information organization and digital records management (2009) 0.27 
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    Date
    29. 8.2009 21:15:48
    Footnote
    Vgl.: http://ieeexplore.ieee.org/Xplore/login.jsp?reload=true&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F4755313%2F4755314%2F04755480.pdf%3Farnumber%3D4755480&authDecision=-203.
  3. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.27 
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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. Li, L.; Shang, Y.; Zhang, W.: Improvement of HITS-based algorithms on Web documents 0.26 
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    Content
    Vgl.: http%3A%2F%2Fdelab.csd.auth.gr%2F~dimitris%2Fcourses%2Fir_spring06%2Fpage_rank_computing%2Fp527-li.pdf. Vgl. auch: http://www2002.org/CDROM/refereed/643/.
  5. Popper, K.R.: Three worlds : the Tanner lecture on human values. Deliverd at the University of Michigan, April 7, 1978 (1978) 0.26 
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    Source
    https%3A%2F%2Ftannerlectures.utah.edu%2F_documents%2Fa-to-z%2Fp%2Fpopper80.pdf&usg=AOvVaw3f4QRTEH-OEBmoYr2J_c7H
  6. Murphy, M.L.: Lexical meaning (2010) 0.24 
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    Date
    22. 7.2013 10:53:30
    RSWK
    Semasiologie
    Semantik / Semasiologie / Lehrbuch (BVB)
    Subject
    Semasiologie
    Semantik / Semasiologie / Lehrbuch (BVB)
  7. Vetere, G.; Lenzerini, M.: Models for semantic interoperability in service-oriented architectures (2005) 0.23 
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    Content
    Vgl.: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5386707&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5386707.
  8. Zeng, Q.; Yu, M.; Yu, W.; Xiong, J.; Shi, Y.; Jiang, M.: Faceted hierarchy : a new graph type to organize scientific concepts and a construction method (2019) 0.19 
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    Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
  9. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.19 
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  10. Xiong, C.: Knowledge based text representations for information retrieval (2016) 0.18 
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    Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.
  11. Huo, W.: Automatic multi-word term extraction and its application to Web-page summarization (2012) 0.17 
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    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
  12. Farazi, M.: Faceted lightweight ontologies : a formalization and some experiments (2010) 0.16 
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    PhD Dissertation at International Doctorate School in Information and Communication Technology. Vgl.: https%3A%2F%2Fcore.ac.uk%2Fdownload%2Fpdf%2F150083013.pdf&usg=AOvVaw2n-qisNagpyT0lli_6QbAQ.
  13. Malsburg, C. von der: ¬The correlation theory of brain function (1981) 0.16 
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    Source
    http%3A%2F%2Fcogprints.org%2F1380%2F1%2FvdM_correlation.pdf&usg=AOvVaw0g7DvZbQPb2U7dYb49b9v_
  14. Stojanovic, N.: Ontology-based Information Retrieval : methods and tools for cooperative query answering (2005) 0.13 
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    Content
    Vgl.: http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F1627&ei=tAtYUYrBNoHKtQb3l4GYBw&usg=AFQjCNHeaxKkKU3-u54LWxMNYGXaaDLCGw&sig2=8WykXWQoDKjDSdGtAakH2Q&bvm=bv.44442042,d.Yms.
  15. Colomb, R.M.: Information spaces : the architecture of cyberspace (2002) 0.05 
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    RSWK
    Informationsmanagement / Lehrbuch
    Information Engineering / Lehrbuch
    Information Retrieval / Lehrbuch
    Dokumentverarbeitung / Lehrbuch
    Dokumentverwaltungssystem / Lehrbuch (BVB)
    Subject
    Informationsmanagement / Lehrbuch
    Information Engineering / Lehrbuch
    Information Retrieval / Lehrbuch
    Dokumentverarbeitung / Lehrbuch
    Dokumentverwaltungssystem / Lehrbuch (BVB)
  16. Aitchison, J.; Gilchrist, A.; Bawden, D.: Thesaurus construction and use : a practical manual (2000) 0.03 
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    Thesaurus / Verwendung / Struktur / Lehrbuch (SWB)
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    Thesaurus / Verwendung / Struktur / Lehrbuch (SWB)
  17. Lemos, N.M.: ¬An introduction to the theory of knowledge (2007) 0.03 
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    Erkenntnistheorie / Lehrbuch (BVB)
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    Erkenntnistheorie / Lehrbuch (BVB)
  18. O'Brien, D.: ¬An introduction to the theory of knowledge (2006) 0.03 
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    Erkenntnistheorie / Wissen / Lehrbuch (BVB)
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    Erkenntnistheorie / Wissen / Lehrbuch (BVB)
  19. Jurafsky, D.; Martin, J.H.: Speech and language processing : ani ntroduction to natural language processing, computational linguistics and speech recognition (2009) 0.02 
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    Computerlinguistik / Automatische Spracherkennung / Lehrbuch
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    Computerlinguistik / Automatische Spracherkennung / Lehrbuch
  20. 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.

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