The probability distribution encoded by the graphical model can then be expressed as follows: Here, F A (x A , y A ) denotes the factors of the model, each of which is of the following form: ensures that this distribution sums up to 1, while λ k are the parameters of the model, which we aim to learn from various annotated background corpora.
our inference objective then is to find the most probable sequence of labels y * when given the token sequence x as evidence: in our setting, we fix x = tok 1 , .