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| Main Authors: | , , |
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| Format: | Recurso educativo Open Access |
| Language: | en |
| Published: |
2001
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| Subjects: | |
| Online Access: | https://eric.ed.gov/?id=ED459814 |
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Table of Contents:
- Text Categorization for Multi-Page Documents: A Hybrid Naive Bayes HMM Approach. Frasconi, Paolo Soda, Giovanni Vullo, Alessandro Classification Document Delivery Electronic Libraries Information Systems Library Collections Periodicals Scholarly Journals Text categorization is typically formulated as a concept learning problem where each instance is a single isolated document. This paper is interested in a more general formulation where documents are organized as page sequences, as naturally occurring in digital libraries of scanned books and magazines. The paper describes a method for classifying pages of sequential OCR text documents into one of several assigned categories and suggests that taking into account contextual information provided by the whole page sequence can significantly improve classification accuracy. The proposed architecture relies on hidden Markov models whose emissions are bag-of-words according to a multinomial word event models, as in the generative portion of the Naive Bayes classifier. Results on a collection of scanned journals from the "Making of America" project confirm the importance of using whole page sequences. Empirical evaluation indicates that the error rate (as obtained by running a plain Naive Bayes classifier on isolated page) can be roughly reduced by half if contextual information is incorporated. (Contains 30 references.) (Author/AEF)