  There have been many recent advances in the structure and measurement of
distributed language models: those that map from words to a vector-space that
is rich in information about word choice and composition. This vector-space is
the distributed language representation. The goal of this note is to point out
that any distributed representation can be turned into a classifier through
inversion via Bayes rule. The approach is simple and modular, in that it will
work with any language representation whose training can be formulated as
optimizing a probability model. In our application to 2 million sentences from
Yelp reviews, we also find that it performs as well as or better than complex
purpose-built algorithms.
