Exploiting structural information for semi-structured document categorization |
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| Authors: | Andrej Bratko Bogdan Filipi? |
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| Institution: | 1. Klika, informacijske tehnologije d.o.o., Stegne 21c, SI-1000 Ljubljana, Slovenia;2. Department of Intelligent Systems, Jozef Stefan Institute, Jamova 39, SI-1000 Ljubljana, Slovenia |
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| Abstract: | This paper examines several different approaches to exploiting structural information in semi-structured document categorization. The methods under consideration are designed for categorization of documents consisting of a collection of fields, or arbitrary tree-structured documents that can be adequately modeled with such a flat structure. The approaches range from trivial modifications of text modeling to more elaborate schemes, specifically tailored to structured documents. We combine these methods with three different text classification algorithms and evaluate their performance on four standard datasets containing different types of semi-structured documents. The best results were obtained with stacking, an approach in which predictions based on different structural components are combined by a meta classifier. A further improvement of this method is achieved by including the flat text model in the final prediction. |
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| Keywords: | Text categorization Semi-structured documents Document structure Stacked generalization Support vector machines |
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