LEARNING DEVICE USING CONDITIONAL RANDOM FIELDS OR GLOBAL CONDITIONAL LOG-LINEAR MODELS, PARAMETER LEARNING METHOD IN THE SAME AND PROGRAM

摘要

PPROBLEM TO BE SOLVED: To improve model performance by improving learning accuracy, in a learning device using Conditional Random Fields or Global Conditional Log-linear Models. PSOLUTION: A learning device includes: a list input part 11 taking in a set of lists including origin vectors of a plurality of symbol series, origin vectors of corresponding correct answer symbol series, and symbol series weights of the respective symbol series as learning data; a parameter initialization part 21 for an objective function; an in-list processing part 22 calculating a linear score by an inner product between a parameter and the origin vector, and calculating an index score from the linear score and the symbol series weight; an objective function calculation part 23 calculating the objective function and a gradient thereof by use of all the index scores and origin vectors calculated in the in-list processing part 22; a convergence decision part 24 deciding convergence of the objective function from the gradient; and a parameter update part 25 updating the parameter. PCOPYRIGHT: (C)2011,JPO&INPIT