2004 ConvolutionKernelsForSRL

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Subject Headings: Convolution Kernel Function, Semantic Role Labeling Task

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Abstract

In this paper we have designed and experimented novel convolution kernels for automatic classification of predicate arguments. Their main property is the ability to process structured representations. Support Vector Machines (SVMs), using a combination of such kernels and the at feature kernel, classify PropBank predicate arguments with accuracy higher than the current argument classification state of-the-art. Additionally, experiments on FrameNet data have shown that SVMs are appealing for the classification of semantic roles even if the proposed kernels do not produce any improvement.

References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2004 ConvolutionKernelsForSRLAlessandro MoschittiA study on Convolution Kernels for Shallow Semantic ParsingProceedings of the 42-nd Conference on Association for Computational Linguistichttp://ai-nlp.info.uniroma2.it/moschitti/articles/ACL2004.pdf2004