This paper describes a new automatic method for Japanese predicate argument structure analysis. The method learns relevant features to assign case roles to the argument of the target predicate using the features of the words located closest to the target predicate under various constraints such as dependency types, words, semantic categories, parts of speech, functional words and predicate voices. We constructed decision lists in which these features were sorted by their learned weights. Using our method, we integrated the tasks of semantic role labeling and zero-pronoun identification, and achieved a 17% improvement compared with a baseline method in a sentence level performance analysis.
REF proposed a model for analyzing predicate-argument structures by using decision lists, which integrated the tasks of semantic role labeling and zero-pronoun identification.
A Japanese Predicate Argument Structure Analysis using Decision Lists
Conference On Empirical Methods In Natural Language Processing
Computer Science