Abstract
This paper proposes a stochastic model for semantic role labeling based on Japanese FrameNet and suggests a method to acquire it by machine learning. The model distinguishes semantic roles which cannot be separated by surface cases. The model receives a sentence and its predicate, identifies its predicate argument structure, then identifies the arguments to be labeled, and finally labels them with adequate semantic roles. The system based on the model achieved 77% precision and 68% recall in identifying the semantic roles of the pre-segmented arguments under the condition that the system labels the role whose certainty is more than the threshold. For more difficult tasks of identifying the arguments which should be labeled and their roles, the system attained 63% precision and 43% recall under the same condition. The system also achieved to label different semantic roles to the arguments whose surface cases are identical.
