Frame-Semantic Role Labeling with Heterogeneous Annotations

Meghana Kshirsagar, Sam Thomson, Nathan Schneider, Jaime Carbonell, Noah A. Smith, Chris Dyer

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

Abstract / Description of output

We consider the task of identifying and labeling the semantic arguments of a predicate that evokes a FrameNet frame. This task is challenging because there are only a few thousand fully annotated sentences for supervised training. Our approach augments an existing model with features derived from FrameNet and PropBank and with partially annotated exemplars from FrameNet. We observe a 4% absolute increase in F1 versus the original model.
Original languageEnglish
Title of host publicationProceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing
Place of PublicationBeijing, China
PublisherAssociation for Computational Linguistics
Number of pages7
Publication statusPublished - 1 Jul 2015


Dive into the research topics of 'Frame-Semantic Role Labeling with Heterogeneous Annotations'. Together they form a unique fingerprint.

Cite this