We show how random vectors and random projection can be implemented in the
usual vector space model to construct a Euclidean semantic space from a French
synonym dictionary. We evaluate theoretically the resulting noise and show the
experimental distribution of the similarities of terms in a neighborhood
according to the choice of parameters. We also show that the Schmidt
orthogonalization process is applicable and can be used to separate homonyms
with distinct semantic meanings. Neighboring terms are easily arranged into
semantically significant clusters which are well suited to the generation of
realistic lists of synonyms and to such applications as word selection for
automatic text generation. This process, applicable to any language, can easily
be extended to collocations, is extremely fast and can be updated in real time,
whenever new synonyms are proposed.Comment: 10 pages,5 figures, 7 tables, 17 reference