Our learning framework is constituted by Support Vector Machines (SVMs) and kernel methods applied to automatically generated syntactic and semantic structures.
we designed (i) a new Semantic Role Kernel (SRK) based on a very fast algorithm; (ii) a new sequence kernel over POS tags to encode shallow syntactic information; (iii) many kernel combinations (to our knowledge no previous work uses so many different kernels) which allow for the study of the role of several linguistic levels in a well defined statistical framework.