Domains Kernels for Text Categorization

In this paper we propose and evaluate a technique to perform semi-supervised learning for Text Categorization. In particular we defined a kernel function, namely the Domain Kernel, that allowed us to plug `external knowledge` into the supervised learning process. External knowledge is acquired from unlabeled data in a totally unsupervised way, and it is represented by means of Domain Models. We evaluated the Domain Kernel in two standard benchmarks for Text Categorization with good results, and we compared its performance with a kernel function that exploits a standard bag-of-words feature representation. The learning curves show that the Domain Kernel allows us to reduce drastically the amount of training data required for learning.