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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 10 of 10 1 Automatic methods to extract latent meanings in large text corpora Pölitz, Christian [Verfasser]; Morik, Katharina [Akademischer Betreuer]; Müller, Heinrich [Gutachter]. - Dortmund : Universitätsbibliothek Dortmund, 2016 DNB Subject Category Language Show details 2 Automatic methods to extract latent meanings in large text corpora ... Pölitz, Christian. - : Technische Universität Dortmund, 2016 BASE Show details 3 Automatic methods to extract latent meanings in large text corpora Pölitz, Christian. - 2016 BASE Show details 4 Combining a rule-based approach and machine learning in a good-example extraction task for the purpose of lexicographic work on contemporary standard German Lemnitzer, Lothar; Pölitz, Christian; Didakowski, Jörg... In: Electronic lexicography in the 21st century: linking lexical data in the digital age. Proceedings of the eLex 2015 conference, 11 - 13 August 2015, Herstmonceux Castle, United Kingdom (2015), 21-31 IDS OBELEX meta Show details 5 Using a Maximum Entropy Classifier to link “good” corpus examples to dictionary senses Geyken, Alexander; Pölitz, Christian; Bartz, Thomas In: Electronic lexicography in the 21st century: linking lexical data in the digital age. Proceedings of the eLex 2015 conference, 11 - 13 August 2015, Herstmonceux Castle, United Kingdom (2015), 304-314 IDS OBELEX meta Show details 6 Using data mining and the CLARIN infrastructure to extend corpus-based linguistic research Bartz, Thomas; Pölitz, Christian; Morik, Katharina. - : Linköping Univ. Electronic Press, 2015 BASE Show details 7 Investigation of word senses over time using linguistic corpora Pölitz, Christian; Bartz, Thomas; Morik, Katharina. - : Springer, 2015 BASE Show details 8 Mining corpora of computer-mediated communication: Analysis of linguistic features in Wikipedia talk pages using machine learning methods Beißwenger, Michael; Lüngen, Harald; Margaretha, Eliza... In: Workshop proceedings of the 12th edition of the Konvens Conference (2014), 42-47 IDS Bibliografie zur Gesprächsforschung Show details 9 Mining corpora of computer-mediated communication: Analysis of linguistic features in Wikipedia talk pages using machine learning methods Lüngen, Harald; Beißwenger, Michael; Pölitz, Christian; Margaretha, Eliza. - 2014 Abstract: Machine learning methods offer a great potential to automatically investigate large amounts of data in the humanities. Our contribution to the workshop reports about ongoing work in the BMBF project KobRA (http://www.kobra.tu-dortmund.de) where we apply machine learning methods to the analysis of big corpora in language-focused research of computer-mediated communication (CMC). At the workshop, we will discuss first results from training a Support Vector Machine (SVM) for the classification of selected linguistic features in talk pages of the German Wikipedia corpus in DeReKo provided by the IDS Mannheim. We will investigate different representations of the data to integrate complex syntactic and semantic information for the SVM. The results shall foster both corpus-based research of CMC and the annotation of linguistic features in CMC corpora. Keyword: Computerunterstützte Kommunikation; ddc:400; Korpus; Onlinecommunity URL: https://hildok.bsz-bw.de/files/276/01_06.pdf https://hildok.bsz-bw.de/frontdoor/index/index/docId/276 https://nbn-resolving.org/urn:nbn:de:gbv:hil2-opus-2930 BASE Hide details 10 Mining corpora of computer-mediated communication: analysis of linguistic features in Wikipedia talk pages using machine learning methods [Online resource] Beißwenger, Michael; Lüngen, Harald; Margaretha, Eliza. IDS-Repository Show details

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