Prepositions are highly polysemous, and their variegated senses encode significant seman- tic information. In this paper we match each preposition’s left- and right context, and their interplay to the geometry of the word vec- tors to the left and right of the preposition. Extracting these features from a large corpus and using them with machine learning models makes for an efficient preposition sense dis- ambiguation (PSD) algorithm, which is com- parable to and better than state-of-the-art on two benchmark datasets. Our reliance on no linguistic tool allows us to scale the PSD al- gorithm to a large corpus and learn sense- specific preposition representations. The cru- cial abstraction of preposition senses as word representations permits their use in downstream applications–phrasal verb paraphrasing and preposition selection–with new state-of- the-art results.