Given P (l=1|z, k, c), the goal of the M-step is to (re)estimate the model parameters, P (l=1|k c ), using maximum likelihood estimation.
p (l=1|k c ) is estimated as follows: where Count(k c ) is the number of times k c appears in the training data, Count(k c , l=1) is the expected number of times k c is the context surrounding an AZp and its antecedent c, and θ is the Laplace smoothing parameter, which we set to 1.