A Semantic Kernel to Exploit Linguistic Knowledge

Improving accuracy in Information Retrieval tasks via semantic information is a complex problem characterized by three main aspects: the document representation model, the similarity estimation metric and the inductive algorithm. In this paper an original kernel function sensitive to external semantic knowledge is defined as a document similarity model. This semantic kernel was tested over a text categorization task, under critical learning conditions (i.e. poor training data). The results of cross-validation experiments suggest that the proposed kernel function can be used as a general model of document similarity for IR