Text classification using word sequence kernel methods

This paper presents a comparison study of two sequence kernels for text classification, namely, all common subsequences and sequence kernel. We consider some variations of the two kernels - kernels based on individual features, linear combination of individual kernels and kernels with a factored representation of features - and evaluate them in text classification by employing them as similarity functions in a support vector machine. A sentence is represented as a sequence of words along with their lemma and part-of-speech tags. Experiments show that sequence kernel has a clear advantage over all common subsequences. Since the main difference between the two kernels lies in the fact that the frequency of words (objects) is considered in sequence kernel but not in all common subsequences, we conclude that the frequency of words is an important factor in the successful application of kernels to text classification.