FEATURE WEIGHT LEARNING APPARATUS, N-BEST SCORING APPARATUS, N-BEST RE-RANKING APPARATUS, AND METHOD AND PROGRAM THEREFOR

摘要

PROBLEM TO BE SOLVED: To enable scoring and re-ranking with high accuracy even if a sparse feature is included in a plurality of features when scoring and re-ranking top N pieces of plausible conversion results related to an input on the basis of a plurality of arbitrarily set features.

SOLUTION: A feature weight learning apparatus uses a plurality of learning data and learns a weight for each of plurality of features that is set arbitrarily as a separate task for each piece of learning data using a multitask learning method. Then the apparatus extracts a feature that is commonly functioning on individual pieces of learning data from among the plurality of features using the weight value as indicator, learns the weight of the feature by focusing on the extracted feature and executes scoring and re-ranking.

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