Clustering Polysemic Subcategorization Frame Distributions Semantically 
 Previous research has demonstrated the utility of clustering in inducing semantic verb classes from undisambiguated corpus data . We describe a new approach which involves clustering subcategorization frame (SCF) distributions using the Information Bottleneck and nearest neighbour methods. In contrast to previous work, we particularly focus on clustering polysemic verbs . A novel evaluation scheme is proposed which accounts for the effect of polysemy on the clusters , offering us a good insight into the potential and limitations of semantically classifying undisambiguated SCF data . 