Patent Document ID: 20120084235
Application ID: 12895014
Patent Flag: 0

Claim One:
1. A structured prediction model learning apparatus 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 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 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 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 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.