Hierarchical Density Order Embeddings
Table 3: Spearman’s correlation for HyperLex nouns.
['[EMPTY]', '[BOLD] FR', '[BOLD] SLQS', '[BOLD] Vis-ID', '[BOLD] WN-B', '[BOLD] w2g', '[BOLD] VOE', '[BOLD] Poin', '[BOLD] HypV', '[BOLD] DOE-S', '[BOLD] DOE-A']
The model DOE-A which uses KL divergence and negative sampling approach S1, S2 and S4 outperforms all other existing models, achieving state-of-the-art performance for the HyperLex noun dataset. The model DOE-S which uses expected likelihood kernel attains a lower score of 0.455 compared to the asymmetric counterpart (DOE-A). This result underscores the importance of asymmetric measures which can capture relationship directionality.