@@ -98,6 +98,8 @@ As mentioned before, all evalution metrics confirmed our initial study conjectur
3. Underperformance for infrequent classes
As in all of Machine Learning tasks, system results will be negatively effected if instances for some output classes are barely existent in the test dataset. In our case, we realized quite early during annotation that this could be the case for the Maslow category "physiological needs" as we had very few instances of this class in our data. The results proved our hypothesis regarding this aspect, as the system performed poorly on the instances that were annotated for "physiological needs". Similarly, Reiss motives that rarely occurred in our test dataset performed roughly equally poor due to the same problem: Underfitting due to lack of sufficient data. To resolve this problem, one would have to ensure a more balanced dataset or even consider leaving out a few classes and restructuring the output classes at use.
4. in *train_evaluation.py* we applied the ML metrics for the train files. Here we could observe that the values for the train data perform slightly better. This is due to the fact that the train files, unlike our test data, consist of only one, uncomplex sentence. More details can be found in the attached file.
