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

Yotaro Watanabe, Masayuki Asahara, Yuji Matsumoto

Abstract: In predicate-argument structure analysis, it is important to capture non-local dependencies among arguments and inter-dependencies between the sense of a predicate and the semantic roles of its arguments. However, no existing approach explicitly handles both non-local dependencies and semantic dependencies between predicates and arguments. In this paper we propose a structured model that overcomes the limitation of existing approaches; the model captures both types of dependencies simultaneously by introducing four types of factors including a global factor type capturing non-local dependencies among arguments and a pairwise factor type capturing local dependencies between a predicate and an argument. In experiments the proposed model achieved competitive results compared to the state-of-the-art systems without applying any feature selection procedure.