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  • × author_ss:"Fankhauser, P."
  1. Fankhauser, P.; Fiedler, N.; Witt, A.: Forschungsdatenmanagement in den Geisteswissenschaften am Beispiel der germanistischen Linguistik (2013) 0.03 
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    Abstract
    Die Kernaufgabe des Instituts für Deutsche Sprache (IDS) ist die Erforschung und Dokumentation der deutschen Sprache. Dazu sammelt und archiviert das IDS einen umfangreichen Bestand an Forschungsprimärdaten in Form von Korpora der geschriebenen und gesprochenen Sprache sowie Sekundärdaten, wie zum Beispiel lexikographische Ressourcen. Dieser Beitrag gibt einen Überblick über den Datenbestand des IDS und die laufenden Forschungskooperationen im Bereich der Langzeitarchivierung. In diesem Kontext wird das im Aufbau befindliche Langzeitarchiv des IDS mit seiner Architektur,den zugrundeliegenden Prinzipien zur Daten- und Metadatenmodellierung sowie den daraus abgeleiteten Erfassungsprozessen vorgestellt. Der Beitrag schließt ab mit einem Ausblick auf die Herausforderungen und Perspektiven des Forschungsdatenmanagements aus Sicht der germanistischen Linguistik.
    Source
    Zeitschrift für Bibliothekswesen und Bibliographie. 60(2013) H.6, S.296-306
  2. 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) 0.02 
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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.
    Date
    12. 6.2016 20:23:08

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