At the same time, these link embedding features maintain dependency parsing improvements similar to the complex, template-based features on word clusters and embeddings by previous work (Koo et al., 2008; <cite>Bansal et al., 2014</cite> ) (up to 9% relative error reduction), and also stack statistically significantly over <cite>them</cite> (up to an additional 5% relative error reduction). Another advantage of this approach (versus <cite>previous work</cite> on feature embeddings or special neural networks for parsing) is that these link embeddings can be imported as off-the-shelf, dense, syntactic features into various other NLP tasks, similar to word embedding features, but now with richer, structured information, and in tasks where plain word embeddings have not proven useful .