Our approach and results are partly independent of this representation, however, in that we take a structuralist approach: we derive new, 'second-order embeddings' by modeling the meaning of words by means of their semantic similarity relations to all other words in the vocabulary (de Saussure, 1916;Rieger, 2003).
future research may in principle substitute the deep-learning architectures for semantics considered here by any other method capable of producing semantic similarity values between lexical units.