Relational Word Embeddings
Table 1: Accuracy and macro-averaged F-Measure, precision and recall on BLESS and DiffVec. 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. All models concatenate their encoded representations with the baseline vector difference of standard FastText word embeddings.
['Encoding', 'Model', 'Reference', 'DiffVec Acc.', 'DiffVec F1', 'DiffVec Prec.', 'DiffVec Rec.', 'BLESS Acc.', 'BLESS F1', 'BLESS Prec.', 'BLESS Rec.']
Our model consistently outperforms the FastText embeddings baseline and comparison systems, with the only exception being the precision score for DiffVec. Despite being completely unsupervised, it is also surprising that our model manages to outperform the knowledge-enhanced embeddings of Retrofitting and Attract-Repel in the BLESS dataset. For DiffVec, let us recall that both these approaches have the unfair advantage of having had WordNet as source knowledge base, used both to construct the test set and to enhance the word embeddings.