  Word embeddings have demonstrated strong performance on NLP tasks. However,
lack of interpretability and the unsupervised nature of word embeddings have
limited their use within computational social science and digital humanities.
We propose the use of informative priors to create interpretable and
domain-informed dimensions for probabilistic word embeddings. Experimental
results show that sensible priors can capture latent semantic concepts better
than or on-par with the current state of the art, while retaining the
simplicity and generalizability of using priors.
