The simplest method to estimate the similarity between two multi-contexts is to compute the inner product of their vector representations in the VSM.
formally, we define a space of dimensionality N in which each dimension is associated with one word from the dictionary, and the multi-context m is represented by a row vector where the function f (t i , m) records whether a particular token t i is used in m. Using this representation we define bag-of-words kernel between multicontexts as the bag-of-words representation does not deal well with lexical variability.