Abstract
In this paper, we present a probabilistic method that can improve the efficiency of document classification when applied to structured documents. The analysis of the structure of a document is the starting point of document classification. Our method is designed to augment other classification schemes and complement pre-filtering information extraction procedures to reduce uncertainties. To this end, a probabilistic distribution on the structure of XML documents is introduced. We show how to parameterise existing learning methods to describe the structure distribution efficiently. The learned distribution is then used to predict the classes of unseen documents. Novelty detection making use of the structure-based distribution function is also discussed. Demonstration on model documents and on Internet XML documents are presented.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
