However, despite impressive results obtained by systems using word embeddings, some issues were largely left unexplored, such as (i) the links between representations delivered through word embeddings vs. lexicographic meaning representations; (ii) the cognitive plausibility of the word embeddings (which is different from testing the agreement with conceptual similarity ratings); and (iii) the ways to acquire word embeddings to deliver common-sense usage of language (more on common-sense knowledge later on).
different from lexicographic resources where the minimal addressable unit of meaning is word sense (the synset), with few notable exceptions (such as NASARI [Camacho-Collados, Pilehvar, and Navigli 2015b] and SENSEEMBED [Iacobacci, Pilehvar, and Navigli 2015]), word embeddings typically describe terms.