SEMAFOR: Frame Argument Resolution with Log-Linear Models

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

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


This paper describes the SEMAFOR system’s performance in the SemEval 2010 task on linking events and their participants in discourse. Our entry is based upon SEMAFOR 1.0 (Das et al., 2010a), a frame-semantic probabilistic parser built from log-linear models. The extended system models null instantiations, including non-local argument reference. Performance is evaluated on the task data with and without gold-standard overt arguments. In both settings, it fares the best of the submitted systems with respect to recall and F1.
Original languageEnglish
Title of host publicationSemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
PublisherAssociation for Computational Linguistics
Number of pages4
Publication statusPublished - 2010


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