Compared to other existing semantic role repositories, FrameNet is characterized by an extremely high number of roles or Frame Elements (FEs), which amount to 8,884 in the last resource release. This represents an interesting issue to investigate both from a theoretical and a practical point of view. In this paper, we analyze the semantics of frame elements by automatically assigning them a set of synsets characterizing the typical FE ﬁllers. We show that the synset repository created for each FE can adequately generalize over the ﬁllers, while providing more informative sense labels than just one generic semantic type. We also evaluate the impact of the enriched FE information on a semantic role labeling task, showing that it can improve classiﬁcation precision, though at the cost of lower recall.
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