PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Semi-Structured Document Classification
Ludovic Denoyer and Patrick Gallinari
In: Encyclopedia of Data Warehousing and Mining (2005) John Wang Editor .

Abstract

Document classification developed over the last ten years, using techniques originating from the pattern recognition and machine learning communities. All these methods do operate on flat text representations where word occurrences are considered independents. The recent paper (Sebastiani, 2002) gives a very good survey on textual document classification. With the development of structured textual and multimedia documents, and with the increasing importance of structured document formats like XML, the document nature is changing. Structured documents usually have a much richer representation than flat ones. They have a logical structure. They are often composed of heterogeneous information sources (e.g. text, image, video, metadata, etc). Another major change with structured documents is the possibility to access document elements or fragments. The development of classifiers for structured content is a new challenge for the machine learning and IR communities. A classifier for structured documents should be able to make use of the different content information sources present in an XML document and to classify both full documents and document parts. It should easily adapt to a variety of different sources (e.g. to different Document Type Definitions). It should be able to scale with large document collections.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:443
Deposited By:Ludovic Denoyer
Deposited On:22 December 2004