The best performing are 200-dimensional embeddings trained with a 5-word context window, achieving a Spearman correlation of 0.524.
the introduction of computationally efficient neural network-based methods for unsupervised learning of word embeddings from large unannotated corpora has been a watershed moment in natural language processing in recent years.