The procedure treats the decision on each of the basic classes as if they were all independent.
the probability for an adjective being polysemous amounts to the product of the probabilities of the adjective belonging to each of the basic classes, as expressed in Equation (4) for two arbitrary given classes, A and B. shows that the distribution of polysemous items predicted by Equation (4) is more similar to the distribution obtained with the best machine learning classifier Table 14 Distribution of polysemous items and absolute numbers, according to the prediction (Equation (4); first column), in the machine learning (ML) results shown in Table 12 (second column), and in the gold standard (GS; third column).