  Distributional semantics models derive word space from linguistic items in
context. Meaning is obtained by defining a distance measure between vectors
corresponding to lexical entities. Such vectors present several problems. In
this paper we provide a guideline for post process improvements to the baseline
vectors. We focus on refining the similarity aspect, address imperfections of
the model by applying the hubness reduction method, implementing relational
knowledge into the model, and providing a new ranking similarity definition
that give maximum weight to the top 1 component value. This feature ranking is
similar to the one used in information retrieval. All these enrichments
outperform current literature results for joint ESL and TOEF sets comparison.
Since single word embedding is a basic element of any semantic task one can
expect a significant improvement of results for these tasks. Moreover, our
improved method of text processing can be translated to continuous distributed
representation of biological sequences for deep proteomics and genomics.
