Labelled network motifs reveal stylistic subtleties in written texts
Table 5: Accuracy rate (%) in discriminating the debates from the European Parliament into two classes (Original and Translated).
['[BOLD] Language  [BOLD] English', '[BOLD] Features', '| [ITALIC] W|', '[BOLD] SVM', '[BOLD] Language  [BOLD] Italian', '[BOLD] Features', '| [ITALIC] W|', '[BOLD] SVM']
To simplify our analysis, we just present the results for the classifier with the best accuracies. They achieved an accuracy of 96.7% using the frequency of 300 function words. However, they did not detect translationese with target languages other than English. Once again, we have found that the characterization by labelled motifs is extracting information about translationese. The gain in performance depends on the language being analyzed: for the English, our method surpasses the traditional one by a margin of 14 percentage points. However, for the Spanish language, the gain is only 1.4 percentage points. In the latter case, however, note that an excellent discrimination can already be obtained with the frequency of the 5 most frequent words.