KAS-term: Extracting Slovene Terms from Doctoral Theses via Supervised Machine Learning
Table 3: MWT classification results (precision, recall, F1) on the positive class for the three areas and overall, both for the exclusive and inclusive annotation mappings.
['[EMPTY]', 'P', 'R', 'F1']
Mapping Comparison Our first experiment on MWTs uses an SVM classifier with an RBF kernel, reporting precision, recall and F1 on the term class obtained via leave-one-thesis-out cross-validation, on each area separately, and all together. We perform an experiment on each of the response variable mappings discussed at the beginning of the section, the exclusive mapping (where only the in-domain label is considered to be positive) and the inclusive one (where only the irrelevant label is considered to be negative). Again, the results per area depict that on both mappings the classification is the simplest in Chemistry where annotators agree among themselves best, followed by Political Science and Computer Science.