As a result, as with supervised CRFs, it is guaranteed that λ has a value that achieves the global maximum of L 1 (λ |Θ).
moreover, we can obtain the same form of gradient as that of supervised CRFs (Sha and Pereira, 2003), that is, we can easily optimize L 1 by using the forward-backward algorithm since this paper solely focuses on a sequence model and a gradient-based optimization algorithm in the same manner as those used in supervised CRF parameter estimation.