Exploiting Syntactic and Shallow Semantic Kernels for Question Answer Classification

We study the impact of syntactic and shallow semantic information in automatic classifi- cation of questions and answers and answer re-ranking. We define (a) new tree struc- tures based on shallow semantics encoded in Predicate Argument Structures (PASs) and (b) new kernel functions to exploit the representational power of such structures with Support Vector Machines. Our ex- periments suggest that syntactic information helps tasks such as question/answer classifi- cation and that shallow semantics gives re- markable contribution when a reliable set of PASs can be extracted, e.g. from answers.