Shallow Semantic Parsing Based on FrameNet, VerbNet and PropBank

This article describes a semantic parser based on FrameNet semantic roles that uses a broad knowledge base created by interconnecting three major resources: FrameNet, VerbNet and PropBank. We link the above resources through a mapping between Intersective Levin classes, which are part of PropBank's annotation, and the FrameNet frames. By using Levin classes, we successfully detect FrameNet semantic roles without relying on the frame information. At the same time, the combined usage of the above resources increases the verb coverage and confers more robustness to our parser. The experiments with Support Vector Machines on automatic Levin class detection suggest that (a) tree kernels are well suited for the task and (b) Intersective Levin classes can be used to improve the accuracy of semantic parsing based on FrameNet roles.

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