  We introduce probabilistic embeddings using Laplacian priors (PELP). The
proposed model enables incorporating graph side-information into static word
embeddings. We theoretically show that the model unifies several previously
proposed embedding methods under one umbrella. PELP generalises graph-enhanced,
group, dynamic, and cross-lingual static word embeddings. PELP also enables any
combination of these previous models in a straightforward fashion. Furthermore,
we empirically show that our model matches the performance of previous models
as special cases. In addition, we demonstrate its flexibility by applying it to
the comparison of political sociolects over time. Finally, we provide code as a
TensorFlow implementation enabling flexible estimation in different settings.
