A kernel-based feature weighting for text classification

Text classification by support vector machines can benefit from semantic smoothing kernels that regard semantic relations among index terms while computing similarity. Adding expansion terms to the vector representation can also improve effectiveness. However, existing semantic smoothing kernels do not employ term expansion. This paper proposes a new non-linear kernel for text classification to exploit semantic relations between terms to add weighted expansion terms.