Frame-Semantic Role Labeling With Heterogeneous Annotations

We consider the task of identifying and labeling the semantic arguments of a predicate that evokes a FrameNet frame. This task is challenging because there are only a few thousand fully annotated sentences for supervised training. Our approach augments an existing model with features derived from FrameNet and PropBank and with partially annotated exemplars from FrameNet. We observe a 4% absolute increase in F-1 versus the original model.