A Study on Convolution Kernels for Shallow Statistic Parsing
A Study on Convolution Kernels for Shallow Statistic Parsing
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DOI：
10.3115/1218955.1218998
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发表时间：
2004-07
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影响因子：
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通讯作者：
Alessandro Moschitti
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文献类型：
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作者：
Alessandro Moschitti
在本文中，我们设计并实验了用于谓词参数自动分类的新型卷积核。它们的主要特性是处理结构化表示的能力。支持向量机（SVM）结合使用此类内核和平面特征内核，对 Prop-Bank 谓词参数进行分类，其准确度高于当前最先进的参数分类。此外，在 FrameNet 数据上的实验表明，即使所提出的内核没有产生任何改进，SVM 对语义角色的分类也很有吸引力。
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 flat feature kernel, classify Prop-Bank 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.