  In this paper, we are mainly concerned with the ability to quickly and
automatically distinguish word senses in dynamic semantic spaces in which new
terms and new senses appear frequently. Such spaces are built '"on the fly"
from constantly evolving data sets such as Wikipedia, repositories of patent
grants and applications, or large sets of legal documents for Technology
Assisted Review and e-discovery. This immediacy rules out supervision as well
as the use of a priori training sets. We show that the various senses of a term
can be automatically made apparent with a simple clustering algorithm, each
sense being a vector in the semantic space. While we only consider here
semantic spaces built by using random vectors, this algorithm should work with
any kind of embedding, provided meaningful similarities between terms can be
computed and do fulfill at least the two basic conditions that terms which
close meanings have high similarities and terms with unrelated meanings have
near-zero similarities.
