This paper presents some experiments in clustering homogeneous XML documents to validate an existing classification or more generally an organisational structure. Our approach integrates techniques for extracting knowledge from docu- ments with unsupervised classification (clustering) of documents. We focus on the feature selection used for representing documents and its impact on the emerging clas- sification. We mix the selection of structured features with fine textual selection based on syntactic characteristics. We illustrate and evaluate this approach with a collection of Inria activity reports for the year 2003. The objective is to cluster projects into larger groups (Themes), based on the keywords or different chapters of these activity reports. We then compare the results of clustering using different feature selections, with the official theme structure used by Inria.
In this paper, w edescribe a novel text classi er that can e ectiv ely cope with structured documents. We report experiments that compare its performance with that of a wellknown probabilistic classi er. Our novel classi er can take adv antage of the information in the structure of document that con ventional, purely term-based classi ers ignore.Conventional classi ers are mostly based on the vector space model of document, which views a document simply as an n-dimensional vector of terms. T o retain the information in the structure, w e ha ve dev eloped a structured vector model, which represents a document with a structured vector, whose elements can be either terms or other structured vectors. With this extended model, we also have improved the well-kno wn probabilistic classi cation method based on the Bernoulli document generation model. Our classi er based on these improvements performes signi cantly better on pre-classi ed samples from the web and the US Patent database than the usual classi ers. We propose a new statistical model for the classification of structured documents and consider its use for multimedia document classification. Its main originality is its ability to simultaneously take into account the structural and the content information present in a structured document, and also to cope with different types of content (text, image, etc). We present experiments on the classification of multilingual pornographic HTML pages using text and image data. The system accurately classifies porn sites from 8 European languages. This corpus has been developed by EADS company in the context of a large Web site filtering application.