In this paper, we apply a weakly-supervised
learning approach for slot tagging using conditional
random fields by exploiting web
search click logs. We extend the constrained
lattice training of Tackstrom et al. (2013) to ¨
non-linear conditional random fields in which
latent variables mediate between observations
and labels. When combined with a novel
initialization scheme that leverages unlabeled
data, we show that our method gives significant improvement over strong supervised and
weakly-supervised baselines.