  Unsupervised word embeddings have been shown to be valuable as features in
supervised learning problems; however, their role in unsupervised problems has
been less thoroughly explored. In this paper, we show that embeddings can
likewise add value to the problem of unsupervised POS induction. In two
representative models of POS induction, we replace multinomial distributions
over the vocabulary with multivariate Gaussian distributions over word
embeddings and observe consistent improvements in eight languages. We also
analyze the effect of various choices while inducing word embeddings on
"downstream" POS induction results.

Computation and Language