Combining geographic and textual contexts to train embeddings leads to better translation performance than using textual or geospatial contexts in isolation.
with a seed dictionary of just 528 Turkish words and monolingual text of just 236k tweets, our supervised method is able to predict correct translation pairs with 67.8% precision.