Then our objective is to extract a list of semantic anchors A that minimizes the Euclidean distance between the two different similarity distributions.
for every word i we calculate the average semantic distribution across all topics: where s i g , s i k is the i-th row of the S g and S k similarity matrix, respectively, representing the similarity distribution between word i and every other word in the vocabulary V .