Learning Graph Embeddings from WordNet-based Similarity Measures
Table 4: F1 scores on all-words WSD tasks.
['Model', 'Senseval2', 'Senseval3', 'SemEval-15']
We evaluate on the following all-words English WSD test sets: Senseval-2 Palmer et al. Senseval-3 Mihalcea et al. Raw WordNet similarities are still the best, but the path2vec models are consistently the second after them (and orders of magnitude faster), outperforming other graph embedding baselines. The largest drop between the original and vector-based measures is for JCN-S, which is also the only one which relies not only on graph but also on external information from a corpus, making it more difficult to approximate (see also Note that both the original graph-based measures and graph embeddings do not outperform the most frequent sense (MFS) baseline, which is in line with the original algorithm Sinha and Mihalcea