  The meaning of a word is closely linked to sociocultural factors that can
change over time and location, resulting in corresponding meaning changes.
Taking a global view of words and their meanings in a widely used language,
such as English, may require us to capture more refined semantics for use in
time-specific or location-aware situations, such as the study of cultural
trends or language use. However, popular vector representations for words do
not adequately include temporal or spatial information. In this work, we
present a model for learning word representation conditioned on time and
location. In addition to capturing meaning changes over time and location, we
require that the resulting word embeddings retain salient semantic and
geometric properties. We train our model on time- and location-stamped corpora,
and show using both quantitative and qualitative evaluations that it can
capture semantics across time and locations. We note that our model compares
favorably with the state-of-the-art for time-specific embedding, and serves as
a new benchmark for location-specific embeddings.
