Paper: Semantic Role Labeling Via FrameNet VerbNet And PropBank

ACL ID P06-1117
Title Semantic Role Labeling Via FrameNet VerbNet And PropBank
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

This article describes a robust seman- tic parser that uses a broad knowledge base created by interconnecting three ma- jor resources: FrameNet, VerbNet and PropBank. The FrameNet corpus con- tains the examples annotated with seman- tic roles whereas the VerbNet lexicon pro- vides the knowledge about the syntac- tic behavior of the verbs. We connect VerbNet and FrameNet by mapping the FrameNet frames to the VerbNet Intersec- tive Levin classes. The PropBank corpus, which is tightly connected to the VerbNet lexicon, is used to increase the verb cov- erage 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.