Hierarchical Embeddings for Hypernymy Detection and Directionality
Table 5: Results (ρ) of HyperScore and state-of-the-art measures and word embedding models on graded lexical entailment.
['[BOLD] Measures  [BOLD] Model', '[BOLD] Measures  [ITALIC] ρ', '[BOLD] Embeddings  [BOLD] Model', '[BOLD] Embeddings  [ITALIC] ρ']
HyperScore outperforms even the previously best word embedding model PARAGRAM by .22, and the previously best measures FR by .27. The reason that HyperVec outperforms all other models is that the hierarchy between hypernym and hypornym within HyperVec differentiates hyponym–hypernym pairs from hypernym–hyponym pairs. For example, the HyperScore for the pairs duck–animal and animal–duck are 3.02 and 0.30, respectively. Thus, the magnitude proportion of the hypernym–hyponym pair duck–animal is larger than that for the pair animal–duck.