Combined Syntactic and Semantic Kernels for Text Classification

The exploitation of syntactic structures and semantic background knowledge has always been an appealing subject in the context of text retrieval and information management. The usefulness of this kind of information has been shown most prominently in highly specialized tasks, such as classification in Question Answering (QA) scenarios. So far, however, additional syntactic or semantic information has been used only individually. In this paper, we propose a principled approach for jointly exploiting both types of information. We propose a new type of kernel, the Semantic Syntactic Tree Kernel (SSTK), which incorporates linguistic structures, e.g. syntactic dependencies, and semantic background knowledge, e.g. term similarity based on WordNet, to automatically learn question categories in QA. We show the power of this approach in a series of experiments with a well known Question Classification dataset.