A novel kernel for text categorization

In this paper we proposed a novel kernel for text categorization. This kernel is an inner product in the feature space generated by all word combinations of specified length. A word combination is a collection of different words co-occurring in the same sentence. The word combination of length k is weighted by the k-th root of the product of the inverse document frequencies (IDF) of its words. A computationally simple and efficient algorithm was proposed to calculate this kernel. We conducted experiments on the 20 Newsgroups dataset. This kernel achieves better performance than the classical word kernel and word-sequence kernel. We also assessed the impact of word combination length on performance.