1. A structured prediction model learning apparatus, having a central processing unit, for learning a structured prediction model used to predict an output structure y corresponding to an input structure x, by using supervised data D L and unsupervised data D U , the structured prediction model learning apparatus comprising: an output candidate graph generator implemented by the central processing unit to generate a supervised data output candidate graph for the supervised data and an unsupervised data output candidate graph for the unsupervised data, by using a set of definition data for generating output candidates identified by a structured prediction problem; a feature vector generator extracting features from the supervised data output candidate graph and the unsupervised data output candidate graph by using a feature extraction template, generating a D-dimensional base-model feature vector f x,y corresponding to a set of the features extracted from the supervised data output candidate graph, dividing a set of the features extracted from the unsupervised data output candidate graph into K subsets, and generating a D k -dimensional auxiliary model feature vector g (k) x,y corresponding to features included in a subset k of the K subsets, where K is a natural number and kε{1, 2,. .. , K}; a parameter generator generating a base-model parameter set λ which includes a first parameter set w formed of D first parameters in one-to-one correspondence with D elements of the base-model feature vector f x,y , generating an auxiliary model parameter set θ (k) formed of D k auxiliary model parameters in one-to-one correspondence with D k elements of the auxiliary model feature vector g (k) x,y , and to generate a set Θ={θ (1) , θ (2) ,. .. , θ (K) } of auxiliary model parameter sets, formed of K auxiliary model parameter sets θ (k) ; an auxiliary model parameter estimating unit estimating the set Θ of auxiliary model parameter sets which minimizes the Bregman divergence having a regularization term obtained from the auxiliary model parameter set θ (k) , between each auxiliary model q k and a reference function {tilde over (r)} (x,y) which is a nonnegative function and indicates the degree of pseudo accuracy of the output structure y corresponding to the input structure x, by using the regularization term and the unsupervised data D U , where the auxiliary model q k is obtained by defining the auxiliary model parameter set θ (k) with a log-linear model; and a base-model parameter estimating unit estimating a base-model parameter set λ which minimizes an empirical risk function defined beforehand, by using the supervised data D L and the set Θ of auxiliary model parameter sets, where the base-model parameter set λ includes a second parameter set v={v 1 , v 2 ,. .. , v K } formed of K second parameters in one-to-one correspondence with K auxiliary models; wherein the auxiliary model parameter estimating unit uses the auxiliary model parameter set θ (k) to obtain an L 1 norm regularization term |θ (k) | 1 , obtains the Bregman divergence having the regularization term as the following empirical generalized relative entropy having a regularization term U ⁡ ( Θ ❘ D U ) = C U ⁢ ∑ k ⁢  θ ( k )  1 + ∑ k ⁢ G ^ D U ⁡ ( r ~ || q k ) where C u is a hyper parameter and Ĝ D U ({tilde over (r)}∥q k ) is a generalized relative entropy obtained by using the unsupervised data D U , and estimates the set Θ of auxiliary model parameter sets which minimizes the empirical generalized relative entropy having the regularization term.