The test is designed as follows: First, for each document in a corpus, a certain percentage of words (0%, 10%, ..., 100%) were randomly selected and replaced by their corresponding POS-tags. Since function words are crucial to authorship attribution, instead of only replacing function words as suggested in <hi xmlns="http://www.tei-c.org/ns/1.0" rend="Quote_Char">Schöch et al. (2020)</hi>, any kind of word (including punctuation) may be replaced. The next step is the standard procedure for Delta test: creating a document-term-matrix, computing the z score of each value in it and then select the most frequent word types as feature to classify documents. The classification was done by a linear SVM classifier with 5-fold cross-validation. Following the results in <hi xmlns="http://www.tei-c.org/ns/1.0" rend="Quote_Char">Evert et al. (2017)</hi>, the 2000 most frequent word types in each corpus were taken as feature for all the classification tasks. A further test was also performed: All POS-tags that replaced their corresponding words were removed from the text and the Delta test were then conducted again. By comparing the classification results of these two tests, we can also determine the contribution of POS-tags to the results of the classification. Since the words in texts are randomly replaced or removed which could introduce some random variation into the results, each of the above-described tests is repeated 10 times.