2004 ConvolutionKernelsForSRL
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- (Moschitti, 2004) ⇒ Alessandro Moschitti. (2004). “A study on Convolution Kernels for Shallow Semantic Parsing.” In: Proceedings of the 42-nd Conference on Association for Computational Linguistic (ACL 2004).
Subject Headings: Convolution Kernel Function, Semantic Role Labeling Task
Notes
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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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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2004 ConvolutionKernelsForSRL | Alessandro Moschitti | A study on Convolution Kernels for Shallow Semantic Parsing | http://ai-nlp.info.uniroma2.it/moschitti/articles/ACL2004.pdf |