Semantic Role Labeling via FrameNet, VerbNet and PropBank

This article describes a robust semantic parser that uses a broad knowledge base created by interconnecting three major resources: FrameNet, VerbNet and PropBank. The FrameNet corpus contains the examples annotated with semantic roles whereas the VerbNet lexicon provides the knowledge about the syntactic behavior of the verbs. We connect VerbNet and FrameNet by mapping the FrameNet frames to the VerbNet Intersective Levin classes. The PropBank corpus, which is tightly connected to the VerbNet lexicon, is used to increase the verb coverage and also to test the effectiveness of our approach. The results indicate that our model is an interesting step towards the design of more robust semantic parsers.

• Type: Conference
• Year: 2006
• Where: ACL
• Authors: Ana-Maria Giuglea, Alessandro Moschitti
• Keywords: ACL 2006, ACL 2007, Broad Knowledge Base, Robust Semantic Parsers, VerbNet Lexicon