Moreover, we define the initial centroid of each cluster as the word vector corresponding either to the definition d tj of the respective sense, or alternatively to the example e tj illustrating the sense.
for each token w i and its context vector u i the algorithm decides whether the token is assigned to one of the sense clusters S j to which previous tokens have been assigned, or whether it is assigned to a new empty cluster, by selecting the option that has the highest probability, which is computed as follows: for a non-empty sense, the probability is proportional to the popularity of the sense (number of tokens it already contains, N j ) and to the weighted sum of two cosine similarities s(•, •): one between the context vector u i of the token and the definition of the sense d tj , and another one between u i and the average context vector of the tokens already assigned to the sense (µ j ).