We start with a linear-chain CRF: Algorithm 1 Dependence Extraction Constraints if influence term > 0 then ensure at least one A and one B label else if A > 0 then ensure at least one B label else if B > 0 then ensure at least one A label end if where T is the number of observations indexed by t, k indexes the feature function f k and weight λ k , and Z x normalizes over the entire input sequence, The CRF performs satisfactorily leading to higher precision classification of the labels than recall.
it is not able to capture some information that we know to be true.