One specificity of distributional thesauri from that perspective is that although the weight between two words is representative of their semantic similarity, we know from work such as (Ferret, 2010;Claveau et al., 2014) that the relevance of the semantic neighbors based on this weight strongly decreases as the rank of the neighbors increases.
our strategy for embedding distributional thesauri is two-fold: first, we build an embedding by relying on methods for embedding graphs, either by exploiting directly their structure or from their representation as matrices; second, we adapt the embedding resulting from the first step according to the specificities of distributional thesauri.