Paper: A Semantic Kernel For Predicate Argument Classification

ACL ID W04-2403
Title A Semantic Kernel For Predicate Argument Classification
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2004

Automatically deriving semantic structures from text is a challenging task for machine learning. The flat feature representations, usu- ally used in learning models, can only partially describe structured data. This makes difficult the processing of the semantic information that is embedded into parse-trees. In this paper a new kernel for automatic clas- sification of predicate arguments has been de- signed and experimented. It is based on sub- parse-trees annotated with predicate argument information from PropBank corpus. This ker- nel, exploiting the convolution properties of the parse-tree kernel, enables us to learn which syntactic structures can be associated with the arguments defined in PropBank. Support Vec- tor Machines (SVMs) using such a kernel clas- sify arguments with a better...