Tailoring Word Embeddings For Bilexical Predictions: An Experimental Comparison
Table 1: Pairwise accuracies for the six relations using the unsupervised, ℓ⋆, ℓ2 and ℓ1 models, using either a distributional bag-of-words representation (BoW) or the skip-gram embeddings (SKG) as initial representation. For ℓ⋆ we show results for the rank that gives best accuracy (with the optimal rank in parenthesis), as well as for ranks k=5 and 10.
['[ITALIC] Rel', '[ITALIC] Type', 'ℓ⋆  [ITALIC] UNS', 'ℓ⋆  [ITALIC] best  [ITALIC] k', 'ℓ⋆  [ITALIC] k=5', 'ℓ⋆  [ITALIC] k=10', 'ℓ⋆ ℓ2', 'ℓ1']
Plus, for the ℓ⋆ regularizer we present results at three different ranks, namely 5, 10 or the rank that obtains the best result for each relation. These highly compressed embeddings perform nearly as good as the best performing model for each relation.