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We use a convolutional neural network to perform authorship identification on a very homogeneous dataset of scientific publications. In order to investigate the effect of domain biases, we obscure words below a certain frequency threshold, retaining only their POS-tags. This procedure improves test performance due to better generalization on unseen data. Using our method, we are able to predict the authors of scientific publications in the same discipline at levels well above chance.
We present a testsuite for POS tagging German web data. Our testsuite provides the original raw text as well as the gold tokenisations and is annotated for parts-of-speech. The testsuite includes a new dataset for German tweets, with a current size of 3,940 tokens. To increase the size of the data, we harmonised the annotations in already existing web corpora, based on the Stuttgart-Tübingen Tag Set. The current version of the corpus has an overall size of 48,344 tokens of web data, around half of it from Twitter. We also present experiments, showing how different experimental setups (training set size, additional out-of-domain training data, self-training) influence the accuracy of the taggers. All resources and models will be made publicly available to the research community.
Wie können Diskursmarker in einem Korpus gesprochener Sprache auffindbar gemacht werden? Was ist Part-of-Speech-Tagging und wie funktioniert es? In diesem Artikel soll anhand der POS-Kategorie Diskursmarker dargestellt werden, wie für das Forschungs- und Lehrkorpus Gesprochenes Deutsch (FOLK) ein Part-of-Speech-Tagging entwickelt wurde, das auf die Annotation typisch gesprochen-sprachlicher Phänomene ausgerichtet ist. Diskursmarker sollen dafür aus der Sicht maschineller Sprachverarbeitung dargestellt werden, d. h. wie eine POS-Kategorie Diskursmarker so definiert werden kann, dass sie automatisch annotiert werden kann. Schließlich soll gezeigt werden, wie man auch weitere Diskursmarker in der Datenbank auffinden kann