<hi xmlns="http://www.tei-c.org/ns/1.0" style="font-size:12pt" xml:space="preserve">The topic models were trained using MALLET (McCallum 2002). As a result, a topic-document distribution and the topics are obtained for each topic model. In a topic-document distribution, each document is represented by an </hi> <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic bold" style="font-size:12pt">N</hi> <hi xmlns="http://www.tei-c.org/ns/1.0" style="font-size:12pt" xml:space="preserve">-dimensional vector, while </hi> <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic bold" style="font-size:12pt">N</hi> <hi xmlns="http://www.tei-c.org/ns/1.0" style="font-size:12pt" xml:space="preserve"> is the number of topics of the topic model. Based on the topic-document distribution, the document classification was performed, and the classification was done as a 10-fold cross-validation with a linear SVM classifier. For the newspaper corpus, the articles were classified according to their thematic classes. For the novel corpus, the novel segments were classified according to their subgenre. The topic coherence was automatically calculated by the Java program Palmetto (Röder et al. 2015), and the first ten most important words of each topic were taken for the calculation. The reference corpus for the calculation of the topic coherence is the lemmatized German Wikipedia. Several topic coherence measures have been implemented in Palmetto. For this work, the Normalized Pointwise Mutual Information (NPMI) based coherence measure proposed in Aletras & Stevenson (2013) was taken. The theoretical range of NPMI based coherence measure is between -1 and 1. The higher the score, the better the topic.</hi>