Therefore we also implemented type-constrained decoding (Täckström et al., 2013), i.e., prune the lattice such that the tweet words observed on the website have one of the tags they were labeled with on the website (soft constraints), or, alternatively, were forced during decoding to have the most frequent tags they were labeled with (hard constraint decoding), thereby focusing on licensed sequences.
we exploit the fact that tweets with URLs provide a one-to-one map between an unlabeled instance and the source of supervision, making this unlabeled tweet-website pairs 3: I iterations 4: k = 1000 pool size 5: v=train(X) base model 6: for i ∈ I do 7: v=train(X) 12: end for 13: return v Figure 1: Semi-supervised learning with not-so-distant supervision, i.e.