Another interesting observation is, when all words in texts are replaced by POS-tags, the classification results improve, relative to a reduction of 90%, in the case of the German and English data, but not for the French data. To understand this situation, the change in the number of word types in each corpus was checked. As presented in Figure 4, when 90% of the words are replaced or removed, there are still around 20,000 word types in each text collection. But when all the words are replaced, only a few dozen types remain. Their number becomes so small that it looks like it is reduced to zero in the Figure 4. Since the classification is based on the most frequent 2000 types, although 90% of the words are replaced or removed, the 2000 features used for classification are still mostly from the remaining 10% of words. In the German and English collections, these words bring apparently noise to the classification task. In contrast, the remaining 10% of words in the French corpus are still able to guarantee a relatively good classification result. From the data in Table 1 we can see that number of authors in the French corpus is smaller, which indicates the classification task on the French corpus is easier. More importantly, as presented in previous studies (e.g., <hi xmlns="http://www.tei-c.org/ns/1.0" rend="Quote_Char">Eder 2015</hi>, <hi xmlns="http://www.tei-c.org/ns/1.0" rend="Quote_Char">Romanov & Grallert 2022</hi>), that pulling random samples of at least 5000 words length out of texts will be sufficient for ensuring reliable authorship attribution. Considering the average length of the French novels is over 80,000 words, when 90% of the words are replaced or removed, the remaining 10% (that is, about 8000 words) is still sufficient to guarantee a good classification result. To clarify this issue, further research would certainly be of great interest.