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ECAI
2006
Springer

Semantic Tree Kernels to Classify Predicate Argument Structures

9 years 3 months ago
Semantic Tree Kernels to Classify Predicate Argument Structures
Recent work on Semantic Role Labeling (SRL) has shown that syntactic information is critical to detect and extract predicate argument structures. As syntax is expressed by means of structured data, i.e. parse trees, its encoding in learning algorithms is rather complex. In this paper, we apply tree kernels to encode the whole predicate argument structure in Support Vector Machines (SVMs). We extract from the sentence syntactic parse the subtrees that span potential argument structures of the target predicate and classify them in incorrect or correct structures by means of tree kernel based SVMs. Experiments on the PropBank collection show that the classification accuracy of correct/incorrect structures is remarkably high and helps to improve the accuracy of the SRL task. This is a piece of evidence that tree kernels provide a powerful mechanism to learn the complex relation between syntax and semantics.
Alessandro Moschitti, Bonaventura Coppola, Daniele
Added 22 Aug 2010
Updated 22 Aug 2010
Type Conference
Year 2006
Where ECAI
Authors Alessandro Moschitti, Bonaventura Coppola, Daniele Pighin, Roberto Basili
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