Previously, unlabeled data is explored to derive useful local-context features such as word clusters (Koo et al., 2008), subtree frequencies (Chen et al., 2009;Chen et al., 2013), and word co-occurrence counts (Zhou et al., 2011;Bansal and Klein, 2011).
exploiting such unlabeled data may introduce more discriminative syntactic knowledge, largely compensating labeled training data.