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Bayesian network model for semi-structured document classification
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DOI：10.1016/j.ipm.2004.04.009.png)
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Recently, a new community has started to emerge around the development of new information research methods for searching and analyzing semi-structured and XML like documents. The goal is to handle both content and structural information, and to deal with different types of information content (text, image, etc.). We consider here the task of structured document classification. We propose a generative model able to handle both structure and content which is based on Bayesian networks. We then show how to transform this generative model into a discriminant classifier using the method of Fisher kernel. The model is then extended for dealing with different types of content information (here text and images). The model was tested on three databases: the classical webKB corpus composed of HTML pages, the new INEX corpus which has become a reference in the field of ad-hoc retrieval for XML documents, and a multimedia corpus of Web pages. (C) 2004 Elsevier Ltd. All rights reserved.
Keyword:
statistical learning
Bayesian networks
categorization
structured documents
XML
machine learning
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