Distinguishing Antonyms and Synonymsin a Pattern-based Neural Network
Table 3: Performance of the AntSynNET models in comparison to the baseline models.
['[BOLD] Model', '[BOLD] Adjective  [BOLD] P', '[BOLD] Adjective  [BOLD] R', '[BOLD] Adjective  [BOLD] F1', '[BOLD] Verb  [BOLD] P', '[BOLD] Verb  [BOLD] R', '[BOLD] Verb  [BOLD] F1', '[BOLD] Noun  [BOLD] P', '[BOLD] Noun  [BOLD] R', '[BOLD] Noun  [BOLD] F1']
Concerning adjectives, the two proposed models significantly outperform the two baselines: The performance of the baselines is around .72 for F1, and the corresponding results for the combined AntSynNET model achieve an improvement of >.06. Regarding nouns, the improvement of the new methods is just .02 F1 in comparison to the R&SiW baseline, but we achieve a much better performance in comparison to the SP baseline, an increase of .37 F1. Regarding verbs, we do not outperform the more advanced R&SiW baseline in terms of the F1 score, but we obtain higher recall scores. In comparison to the SP baseline, our models still show a clear F1 improvement.