A semi-supervised approach to question classification

This paper presents a machine learning approach to question classification. We have defined a kernel function based on latent semantic information acquired from unlabeled data. This kernel allows including external semantic knowledge into the supervised learning process. We have combined this knowledge with a bag-of-words approach by means of composite kernels to obtain state-of-the-art results. As the semantic information is acquired from unlabeled text, our system can be easily adapted to different languages and domains.