We aim at showing that lexical descriptions based on multifactorial and continuous models can be used by linguists and lexicographers (and not only by machines) so long as they are provided with a way to efficiently navigate data collections. It is widely accepted that word description cannot be achieved without the analysis of a large number of contexts extracted from real corpora. The recent success of deep learning approaches has shown that discrete representations of the lexicon are no longer appropriate. Continuous models offer a better representation of word meaning, because they encode intuitively valid and cognitively plausible principles: semantic similarity is relative, context-sensitive and depends on multiple-cue integration. We propose tod demonstrate such a system that is usable both by machines and by humans.