Using Belief Networks and Fisher Kernels for Structured Document Classification

摘要

We consider the classification of structured (e.g. XML) textual documents. We first propose a generative model based on Belief Networks which allows us to simultaneously take into account structure and content information. We then show how this model can be extended into a more efficient classifier using the Fisher kernel method. In both cases model parameters are learned from a labelled training set of representative documents. We present experiments on two collections of structured documents: WebKB which has become a reference corpus for HTML page classification and the new INEX corpus which has been developed for the evaluation of XML information retrieval systems.