Word representations and more recently, word embeddings, learned from large amounts of text have been quite successful as features in various NLP tasks (Koo et al., 2008; Turian et al., 2010; Collobert et al., 2011; Dhillon et al., 2012; Al-Rfou' et al., 2013; <cite>Bansal et al., 2014</cite>; Guo et al., 2014; Pennington et al., 2014; Yu and Dredze, 2014; Faruqui et al., 2014; Wang et al., 2015) . While these word representations do capture useful, dense relationships among known and unknown words, one still has to work with sparse conjunctions of features on the multiple words involved in the substructure that a task factors on, e.g., head-argument links in dependency parsing. Therefore, most statistical dependency parsers still suffer from millions of such conjoined, template-based, n-ary features on word clusters or embeddings (Koo et al., 2008; <cite>Bansal et al., 2014</cite>) .