  In this paper we explore the "vector semantics" problem from the perspective
of "almost orthogonal" property of high-dimensional random vectors. We show
that this intriguing property can be used to "memorize" random vectors by
simply adding them, and we provide an efficient probabilistic solution to the
set membership problem. Also, we discuss several applications to word context
vector embeddings, document sentences similarity, and spam filtering.
