This shows how, in contrast to the state of the art (Kozlowski et al., 2018;Garg et al., 2018;Lauretig, 2019;Zhao et al., 2018), informative priors allow easy integration with other, more complex, probabilistic models.
lack of interpretability and the unsupervised nature of word embeddings have limited their use within computational social science and digital humanities.