Specialising Word Vectors for Lexical Entailment
Table 1: L2 norms for selected concepts from the WordNet hierarchy. Input: fastText; lear: D2.
['terrier', '[BOLD] Norm 0.87', 'laptop', '[BOLD] Norm 0.60', 'cabriolet', '[BOLD] Norm 0.74']
To verify that the knowledge concerning the position in the semantic hierarchy actually arises from vector norms, we also manually inspect the norms after lear specialisation. They indicate a desirable pattern in the norm values which imposes a hierarchical ordering on the concepts. Note that the original distributional SGNS model Mikolov et al. However, these norms are not at all correlated with the desired hierarchical ordering, and are therefore useless for le-related applications: the non-specialised distributional SGNS model scores 0.44, 0.48, and 0.34 on the three tasks, respectively.