Although distributional models have evolved over time, from count-based (Landauer et al., 1998) and generative (Blei et al., 2003) to prediction-based (Mikolov et al., 2013), the similarity between distributional vectors still indicates only the abstract semantic association and not a precise semantic relation (e.g., vectors of antonyms may be as similar as vectors of synonyms).
a number of approaches have been proposed for specializing distributional spaces for specific lexico-semantic relations, either by (1) modifying the learning objective or regularization of the original embedding model by incorporating linguistic constraints (Yu and Dredze, 2014;Kiela et al., 2015) or (2) retroactively fitting the pre-trained unspecialized embeddings to linguistic constraints (Faruqui et al., 2015;Mrkšić et al., 2016).