Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Semantic role classification accuracy for most languages other than English is constrained by the small amount of annotated data. ,  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 Sdish, a low-resource language. In order to make use of the FrameNet relations,  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%.
['Richard Johansson']
I am writing a research paper on a idea Non-atomic Classification to Improve a Semantic Role Labeler for a Low-resource Language
[{"from": "human", "value": "I am writing a research paper on a idea Non-atomic Classification to Improve a Semantic Role Labeler for a Low-resource Language"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Semantic role classification accuracy for most languages other than English is constrained by the small amount of annotated data. ,  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 Sdish, a low-resource language. In order to make use of the FrameNet relations,  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%."}]