Evaluating Neural Word Representations inTensor-Based Compositional Settings
Table 3: Spearman ρ correlations of models with human judgements for the word sense disambiguation task. The best result (NWE Copy object) outperforms the nearest co-occurrence-based competitor (KS14 Frobenius outer) with a statistically significant difference (p<0.05, t-test).
['[BOLD] Method', '[BOLD] GS11', '[BOLD] KS14', '[BOLD] NWE']
NWE copy-object composition yields the best correlation with the human judgements, and top performance across all vector spaces and models with a Spearman ρ of 0.456. For the KS14 space, the best result comes from Frobenius outer (0.350), while the best operator for the GS11 space is point-wise multiplication (0.348).