Recently, Levy and Goldberg (2014) have generalized the skip-gram model to include not only linear but arbitrary contexts like contexts derived from dependency parse trees.
positive training examples are collected by checking if the 500 nearest text-based neighbors of w contain one of the antonyms listed by Webster.