A new objective function for sequence labeling

We propose a new loss function for discriminative learning of Markov random fields, which is an intermediate loss function between the sequential loss and the pointwise loss. We show this loss function has ldquoMarkov propertyrdquo, that is, the importance of correct labeling for a particular position depends on the numbers of the correct labels around there. This property works to keep local consistencies among the assigned labels, and is useful for optimizing systems identifying structural segments, such as information extraction systems.