Structured prediction model learning apparatus, method, program, and recording medium

A structured prediction model learning method for learning a structured prediction model used to predict an output structure y corresponding to an input structure x, by using supervised data DL and unsupervised data DU, the structured prediction model learning method comprising:

• an output candidate graph generating step of generating 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 generating step of 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 fx,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 Dk-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 generating step of 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 fx,y, generating an auxiliary model parameter set θ(k) formed of Dk auxiliary model parameters in one-to-one correspondence with Dk elements of the auxiliary model feature vector g(k)x,y, and generating 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 step of estimating a 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 qk 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 DU, where the auxiliary model qk is obtained by defining the auxiliary model parameter set θ(k) with a log-linear model;
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• a base-model parameter estimating step of estimating a base-model parameter set λ which minimizes an empirical risk function defined beforehand, by using the supervised data DL and the set Θ of auxiliary model parameter sets, where the base-model parameter set λ includes a second parameter set v={v1, v2, . . . , vK} formed of K second parameters in one-to-one correspondence with K auxiliary models.