Probabilistic Frame-Semantic Parsing

Dipanjan Das, Nathan Schneider, Desai Chen, Noah A. Smith

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationHuman Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, June 2-4, 2010, Los Angeles, California, USA
PublisherAssociation for Computational Linguistics
Pages948-956
Number of pages9
Publication statusPublished - 2010

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