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  • × theme_ss:"Automatisches Klassifizieren"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.25
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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. Reiner, U.: Automatische DDC-Klassifizierung bibliografischer Titeldatensätze der Deutschen Nationalbibliografie (2009) 0.04
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    Abstract
    Das Klassifizieren von Objekten (z. B. Fauna, Flora, Texte) ist ein Verfahren, das auf menschlicher Intelligenz basiert. In der Informatik - insbesondere im Gebiet der Künstlichen Intelligenz (KI) - wird u. a. untersucht, inweit Verfahren, die menschliche Intelligenz benötigen, automatisiert werden können. Hierbei hat sich herausgestellt, dass die Lösung von Alltagsproblemen eine größere Herausforderung darstellt, als die Lösung von Spezialproblemen, wie z. B. das Erstellen eines Schachcomputers. So ist "Rybka" der seit Juni 2007 amtierende Computerschach-Weltmeistern. Inwieweit Alltagsprobleme mit Methoden der Künstlichen Intelligenz gelöst werden können, ist eine - für den allgemeinen Fall - noch offene Frage. Beim Lösen von Alltagsproblemen spielt die Verarbeitung der natürlichen Sprache, wie z. B. das Verstehen, eine wesentliche Rolle. Den "gesunden Menschenverstand" als Maschine (in der Cyc-Wissensbasis in Form von Fakten und Regeln) zu realisieren, ist Lenat's Ziel seit 1984. Bezüglich des KI-Paradeprojektes "Cyc" gibt es CycOptimisten und Cyc-Pessimisten. Das Verstehen der natürlichen Sprache (z. B. Werktitel, Zusammenfassung, Vorwort, Inhalt) ist auch beim intellektuellen Klassifizieren von bibliografischen Titeldatensätzen oder Netzpublikationen notwendig, um diese Textobjekte korrekt klassifizieren zu können. Seit dem Jahr 2007 werden von der Deutschen Nationalbibliothek nahezu alle Veröffentlichungen mit der Dewey Dezimalklassifikation (DDC) intellektuell klassifiziert.
    Date
    22. 1.2010 14:41:24
  3. Pfeffer, M.: Automatische Vergabe von RVK-Notationen mittels fallbasiertem Schließen (2009) 0.03
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    Abstract
    Klassifikation von bibliografischen Einheiten ist für einen systematischen Zugang zu den Beständen einer Bibliothek und deren Aufstellung unumgänglich. Bislang wurde diese Aufgabe von Fachexperten manuell erledigt, sei es individuell nach einer selbst entwickelten Systematik oder kooperativ nach einer gemeinsamen Systematik. In dieser Arbeit wird ein Verfahren zur Automatisierung des Klassifikationsvorgangs vorgestellt. Dabei kommt das Verfahren des fallbasierten Schließens zum Einsatz, das im Kontext der Forschung zur künstlichen Intelligenz entwickelt wurde. Das Verfahren liefert für jedes Werk, für das bibliografische Daten vorliegen, eine oder mehrere mögliche Klassifikationen. In Experimenten werden die Ergebnisse der automatischen Klassifikation mit der durch Fachexperten verglichen. Diese Experimente belegen die hohe Qualität der automatischen Klassifikation und dass das Verfahren geeignet ist, Fachexperten bei der Klassifikationsarbeit signifikant zu entlasten. Auch die nahezu vollständige Resystematisierung eines Bibliothekskataloges ist - mit gewissen Abstrichen - möglich.
    Date
    22. 8.2009 19:51:28
  4. Pfeffer, M.: Automatische Vergabe von RVK-Notationen anhand von bibliografischen Daten mittels fallbasiertem Schließen (2007) 0.01
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    Abstract
    Klassifikation von bibliografischen Einheiten ist für einen systematischen Zugang zu den Beständen einer Bibliothek und deren Aufstellung unumgänglich. Bislang wurde diese Aufgabe von Fachexperten manuell erledigt, sei es individuell nach einer selbst entwickelten Systematik oder kooperativ nach einer gemeinsamen Systematik. In dieser Arbeit wird ein Verfahren zur Automatisierung des Klassifikationsvorgangs vorgestellt. Dabei kommt das Verfahren des fallbasierten Schließens zum Einsatz, das im Kontext der Forschung zur künstlichen Intelligenz entwickelt wurde. Das Verfahren liefert für jedes Werk, für das bibliografische Daten vorliegen, eine oder mehrere mögliche Klassifikationen. In Experimenten werden die Ergebnisse der automatischen Klassifikation mit der durch Fachexperten verglichen. Diese Experimente belegen die hohe Qualität der automatischen Klassifikation und dass das Verfahren geeignet ist, Fachexperten bei der Klassifikationsarbeit signifikant zu entlasten. Auch die nahezu vollständige Resystematisierung eines Bibliothekskataloges ist - mit gewissen Abstrichen - möglich.
  5. Kasprzik, A.: Automatisierte und semiautomatisierte Klassifizierung : eine Analyse aktueller Projekte (2014) 0.01
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    Abstract
    Das sprunghafte Anwachsen der Menge digital verfügbarer Dokumente gepaart mit dem Zeit- und Personalmangel an wissenschaftlichen Bibliotheken legt den Einsatz von halb- oder vollautomatischen Verfahren für die verbale und klassifikatorische Inhaltserschließung nahe. Nach einer kurzen allgemeinen Einführung in die gängige Methodik beleuchtet dieser Artikel eine Reihe von Projekten zur automatisierten Klassifizierung aus dem Zeitraum 2007-2012 und aus dem deutschsprachigen Raum. Ein Großteil der vorgestellten Projekte verwendet Methoden des Maschinellen Lernens aus der Künstlichen Intelligenz, arbeitet meist mit angepassten Versionen einer kommerziellen Software und bezieht sich in der Regel auf die Dewey Decimal Classification (DDC). Als Datengrundlage dienen Metadatensätze, Abstracs, Inhaltsverzeichnisse und Volltexte in diversen Datenformaten. Die abschließende Analyse enthält eine Anordnung der Projekte nach einer Reihe von verschiedenen Kriterien und eine Zusammenfassung der aktuellen Lage und der größten Herausfordungen für automatisierte Klassifizierungsverfahren.
  6. Teich, E.; Degaetano-Ortlieb, S.; Fankhauser, P.; Kermes, H.; Lapshinova-Koltunski, E.: ¬The linguistic construal of disciplinarity : a data-mining approach using register features (2016) 0.01
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    Abstract
    We analyze the linguistic evolution of selected scientific disciplines over a 30-year time span (1970s to 2000s). Our focus is on four highly specialized disciplines at the boundaries of computer science that emerged during that time: computational linguistics, bioinformatics, digital construction, and microelectronics. Our analysis is driven by the question whether these disciplines develop a distinctive language use-both individually and collectively-over the given time period. The data set is the English Scientific Text Corpus (scitex), which includes texts from the 1970s/1980s and early 2000s. Our theoretical basis is register theory. In terms of methods, we combine corpus-based methods of feature extraction (various aggregated features [part-of-speech based], n-grams, lexico-grammatical patterns) and automatic text classification. The results of our research are directly relevant to the study of linguistic variation and languages for specific purposes (LSP) and have implications for various natural language processing (NLP) tasks, for example, authorship attribution, text mining, or training NLP tools.
  7. Subramanian, S.; Shafer, K.E.: Clustering (2001) 0.00
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    Date
    5. 5.2003 14:17:22
  8. Ibekwe-SanJuan, F.; SanJuan, E.: From term variants to research topics (2002) 0.00
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    Abstract
    In a scientific and technological watch (STW) task, an expert user needs to survey the evolution of research topics in his area of specialisation in order to detect interesting changes. The majority of methods proposing evaluation metrics (bibliometrics and scientometrics studies) for STW rely solely an statistical data analysis methods (Co-citation analysis, co-word analysis). Such methods usually work an structured databases where the units of analysis (words, keywords) are already attributed to documents by human indexers. The advent of huge amounts of unstructured textual data has rendered necessary the integration of natural language processing (NLP) techniques to first extract meaningful units from texts. We propose a method for STW which is NLP-oriented. The method not only analyses texts linguistically in order to extract terms from them, but also uses linguistic relations (syntactic variations) as the basis for clustering. Terms and variation relations are formalised as weighted di-graphs which the clustering algorithm, CPCL (Classification by Preferential Clustered Link) will seek to reduce in order to produces classes. These classes ideally represent the research topics present in the corpus. The results of the classification are subjected to validation by an expert in STW.
  9. Smiraglia, R.P.; Cai, X.: Tracking the evolution of clustering, machine learning, automatic indexing and automatic classification in knowledge organization (2017) 0.00
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  10. Reiner, U.: Automatische DDC-Klassifizierung von bibliografischen Titeldatensätzen (2009) 0.00
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    Date
    22. 8.2009 12:54:24
  11. HaCohen-Kerner, Y. et al.: Classification using various machine learning methods and combinations of key-phrases and visual features (2016) 0.00
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    Date
    1. 2.2016 18:25:22
  12. Bock, H.-H.: Datenanalyse zur Strukturierung und Ordnung von Information (1989) 0.00
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    Pages
    S.1-22
  13. Dubin, D.: Dimensions and discriminability (1998) 0.00
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    Date
    22. 9.1997 19:16:05
  14. Automatic classification research at OCLC (2002) 0.00
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    Date
    5. 5.2003 9:22:09
  15. Jenkins, C.: Automatic classification of Web resources using Java and Dewey Decimal Classification (1998) 0.00
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    Date
    1. 8.1996 22:08:06
  16. Yoon, Y.; Lee, C.; Lee, G.G.: ¬An effective procedure for constructing a hierarchical text classification system (2006) 0.00
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  17. Yi, K.: Automatic text classification using library classification schemes : trends, issues and challenges (2007) 0.00
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