6. A correspondence learning apparatus according to claim 1 , wherein the information deriving module includes a probability density distribution setting module that sets a probability density distribution indicating a probability of occurrence of the arbitrary first feature from the latent variable for each of the latent variables, and a symbol occurrence probability calculating module that calculates a symbol occurrence probability being a probability of occurrence of the symbol from the latent variable for each of symbols different from each other among all of the second data, and wherein the information storage stores the probability density distribution and the symbol occurrence probability as the information for each of the latent variables.
6. A correspondence learning apparatus according to claim 1 , wherein the information deriving module includes a probability density distribution setting module that sets a probability density distribution indicating a probability of occurrence of the arbitrary first feature from the latent variable for each of the latent variables, and a symbol occurrence probability calculating module that calculates a symbol occurrence probability being a probability of occurrence of the symbol from the latent variable for each of symbols different from each other among all of the second data, and wherein the information storage stores the probability density distribution and the symbol occurrence probability as the information for each of the latent variables. 7. A correspondence learning apparatus according to claim 6 , wherein the probability density distribution setting module sets a normal distribution around the latent variable.