  Prepositions are highly polysemous, and their variegated senses encode
significant semantic information. In this paper we match each preposition's
complement and attachment and their interplay crucially to the geometry of the
word vectors to the left and right of the preposition. Extracting such features
from the vast number of instances of each preposition and clustering them makes
for an efficient preposition sense disambigution (PSD) algorithm, which is
comparable to and better than state-of-the-art on two benchmark datasets. Our
reliance on no external linguistic resource allows us to scale the PSD
algorithm to a large WikiCorpus and learn sense-specific preposition
representations -- which we show to encode semantic relations and paraphrasing
of verb particle compounds, via simple vector operations.
