In related work, <cite>Bansal et al. (2014)</cite> also use dependency context to tailor word embeddings to dependency parsing. However, <cite>their</cite> embedding features are still based on the sparse set of n-ary, word-based templates from previous work (McDonald et al., 2005a; Koo et al., 2008) . Our structured link embeddings achieve similar improvements <cite>as theirs</cite> (and better in the case of direct, per-dimension bucket features) with a substantially smaller and simpler (unary) set of features that are aimed to directly capture hidden relationships between the substructures that dependency parsing factors on.