Offensive Language Identification in Greek
Table 3: Results for offensive language detection with TF/IDF bigram features. For each model, Precision (P), Recall (R), and F1 are reported on all classes, and weighted averages. Macro-F1 is also listed (best in bold).
['[BOLD] Model', '[BOLD] Not Offensive  [BOLD] P', '[BOLD] Not Offensive  [BOLD] R', '[BOLD] Not Offensive  [BOLD] F1', '[BOLD] Offensive  [BOLD] P', '[BOLD] Offensive  [BOLD] R', '[BOLD] Offensive  [BOLD] F1', '[BOLD] Weighted Average  [BOLD] P', '[BOLD] Weighted Average  [BOLD] R', '[BOLD] Weighted Average  [BOLD] F1', '[BOLD] F1 Macro']
Models trained with TF/IDF bigram features performed worse, with scores of all evaluation metrics dropping with the exception of Multinomial Naïve Bayes which improved in F1-score for the Not Offensive class. Three other approaches were opted for training the models with the implementation of POS and dependency relation tags via a transformation pipeline, also including TF/IDF unigram features, performing better than the addition of bigrams.