In order to evaluate the automatic clustering procedure that uses K-means++ and word vectors, we start with the gold standard provided by de Melo and Bansal (2013): as mentioned above, their data set has 88 gold standard clusters, corresponding to 346 adjectives, annotated by humans for scale ordering.
this experiment uses hand-corrected WordNet dumbbells to determine adjectives on the same scale of semantic intensity, followed by the MILP using strength counts from the Google N-gram corpus, to determine the ranking.