Extending and Improving Wordnet via Unsupervised Word Embeddings
Table 3: Russian Wordnet Results
['Method', 'POS', '[ITALIC] F∗.5', 'Prec.∗', 'Rec.∗', 'Coverage', 'Synsets']
Parameters α and β are tuned to maximize micro-average F.5-score 1.25⋅Precision⋅Recall.25⋅% Precision+Recall, used instead of F1 to prioritize precision (often more important for applications). Linear-WSI heuristics further improve F.5-score by 1% in Russian and 2% for French, exceeding WOLF in F.5-score across POS while having similar coverage. Notably, OMW consistently achieves best precision, although it and UWN have low recall and coverage.