A Structured Prediction Model for Joint Learning of Predicate Senses and Argument Roles

Abstract

The two subtasks of predicate-argument structure analysis -- argument role classification and predicate word sense disambiguation, are mutually related. Information of argument roles is useful for predicate word sense disambiguation, at the same time, the predicate sense information can be an important clue for argument role labeling. However, most of the existing approaches do not model such structural interdependencies. In this paper, we propose a structured prediction model that learns predicate word senses and argument roles simultaneously. In order to deal with the structural interdependencies, we introduce two factors: pairwise factor that captures local dependencies between predicates and arguments, and global factor that captures non-local dependencies over whole predicate-argument structure. We propose a new large-margin learning algorithm for linear models, in which the global factor is handled in parallel with the local factor. In the experiments, the proposed model achieved performance improvements in both tasks, and competitive results compare to the state-of-the-art systems.