  The aim of this paper is the supervised classification of semi-structured
data. A formal model based on bayesian classification is developed while
addressing the integration of the document structure into classification tasks.
We define what we call the structural context of occurrence for unstructured
data, and we derive a recursive formulation in which parameters are used to
weight the contribution of structural element relatively to the others. A
simplified version of this formal model is implemented to carry out textual
documents classification experiments. First results show, for a adhoc weighting
strategy, that the structural context of word occurrences has a significant
impact on classification results comparing to the performance of a simple
multinomial naive Bayes classifier. The proposed implementation competes on the
Reuters-21578 data with the SVM classifier associated or not with the splitting
of structural components. These results encourage exploring the learning of
acceptable weighting strategies for this model, in particular boosting
strategies.
