Hoppa till huvudinnehåll


	title        = {Non-atomic Classification to Improve a Semantic Role Labeler for a Low-resource Language},
	abstract     = {Semantic role classification accuracy for most languages other than English is constrained by the small amount of annotated data. In this paper, we demonstrate how the frame-to-frame relations described in the FrameNet ontology can be used to improve the performance of a FrameNet-based semantic role classifier for Swedish, a low-resource language. In order to make use of the FrameNet relations, we cast the semantic
role classification task as a non-atomic label prediction task.

The experiments show that the cross-frame generalization methods lead to a 27% reduction in the number of errors made by the classifier. For previously unseen frames, the reduction is even more significant: 50%.
	booktitle    = {Proceedings of the First Joint Conference on Lexical and Computational Semantics (*SEM); June 7-8; Montréal, Canada},
	author       = {Johansson, Richard},
	year         = {2012},
	publisher    = {Association for Computational Linguistics},
	address      = {Montréal, Canada},