Relational Word Embeddings
Table 2: Results on the McRae feature norms dataset (Macro F-Score) and QVEC (correlation score). Models marked with † use external resources. The results with * indicate that WordNet was used for both the development of the model and the construction of the dataset.
['Model', 'McRae Feature Norms Overall', 'McRae Feature Norms metal', 'McRae Feature Norms is_small', 'McRae Feature Norms is_large', 'McRae Feature Norms animal', 'McRae Feature Norms is_edible', 'McRae Feature Norms wood', 'McRae Feature Norms is_round', 'McRae Feature Norms is_long', 'QVEC']
In the case of the McRae Feature Norms dataset, our relational word embeddings achieve the best overall results, although there is some variation for the individual features. These results suggest that attributional information is encoded well in our relational word embeddings. Interestingly, our results also suggest that Retrofitting and Attract-Repel, which use pairs of related words during training, may be too naïve to capture the complex relationships proposed in these benchmarks. In fact, they perform considerably lower than the baseline FastText model. On the other hand, Pair2Vec, which we recall is the most similar to our model, yields slightly better results than the FastText baseline, but still worse than our relational word embedding model. This is especially remarkable considering its much lower computational cost.