<hi xmlns="http://www.tei-c.org/ns/1.0" style="font-size:12pt">By performing Bag-of-Words (BoW) model-based classification, a baseline of document classification has been defined for both corpora. The tests were also done as a 10-fold cross-validation with a linear SVM classifier. The F1(macro) score for the newspaper articles and for the novel segments was 0.758 and 0.993, respectively. A baseline of the NPMI value was also defined for each corpus. With only one iteration, 14 topic models were first trained on each corpus, containing 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500 topics, respectively. In this way, 1,950 “topics before topic modeling” have been trained for each corpus. The NPMI scores of these topics were then calculated, and the average NPMI score is the NPMI baseline, which is -0.0619 for the newspaper corpus and -0.1153 for the novel corpus. A black line represents the baselines in Figure 3 and Figure 4.</hi>