Machine Learning for Semi-Structured Multimedia Documents : Application to pornographic filtering and thematic categorization
Abstract
We propose a generative statistical model for the classification of semi-structured multimedia documents. Its main originality is its ability to simultaneously take into account the structural and the content information present in a semi-structured document and also to cope with different types of content (text, image, etc.). We then present the results obtained on two sets of experiments:
• One set concerns the filtering of pornographic Web pages
• The second one concerns the thematic classification of Wikipedia documents.