Considering embeddings only, Levy and Goldberg (2014) proposed to learn word representations that incorporate syntax from dependency-based contexts.
we inject syntactic information by means of TKs, which establish a hard match between tree fragments, while the soft match is enabled by the similarities of distributed representations.