Probabilistic Frame-Semantic Parsing

This paper contributes a formalization of frame-semantic parsing as a structure prediction problem and describes an implemented parser that transforms an English sentence into a frame-semantic representation. It finds words that evoke FrameNet frames, selects frames for them, and locates the arguments for each frame. The system uses two featurebased, discriminative probabilistic (log-linear) models, one with latent variables to permit disambiguation of new predicate words. The parser is demonstrated to significantly outperform previously published results.

• Type: Journal
• Year: 2010
• Where: NAACL
• Authors: Dipanjan Das, Nathan Schneider, Desai Chen, Noah A. Smith
• Keywords: Computational Linguistics, English Sentence, Implemented Parser, NAACL 2010, Structure Prediction Problem