Robust semantic analysis for unseen data in FrameNet
We present a novel method for FrameNetbased semantic role labeling (SRL), focusing on limitations posed by the limited coverage of available annotated data. Our SRL model is based on Bayesian clustering and has the advantage of being very robust in the face of unseen and incomplete data. Frame labeling and role labeling are modeled in like fashions, allowing cascading classification scenarios. The model is shown to perform especially well on unseen data. In addition, we show that for seen data, predicting semantic types for roles improves role labeling performance.