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Text Categorization for Multi-Page Documents [microform] : A Hybrid Naive Bayes HMM Approach / Paolo Frasconi, Giovanni Soda and Alessandro Vullo
Bib ID 5687371
Format BookBook, MicroformMicroform, OnlineOnline
Author
Frasconi, Paolo
 
Online Versions
Description [Washington, D.C.] : Distributed by ERIC Clearinghouse, 2001 
12 p. 
Summary

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)

Notes

Availability: Association for Computing Machinery, 1515 Broadway, New York NY 10036. Tel: 800-342-6626 (Toll Free); Tel: 212-626-0500; e-mail: acmhelp@acm.org. For full text: http://www1.acm.org/pubs/contents/proceedings/dl/379437/.

Contract Number: IST-1999-20021.

ERIC Note: In: Proceedings of the ACM/IEEE-CS Joint Conference on Digital Libraries (1st, Roanoke, Virginia, June 24-28, 2001). For entire proceedings, see IR 058 348. Supported by EC grant under METAe project.

May also be available online. Address as at 14/8/18: https://eric.ed.gov/

Reproduction Microfiche. [Washington D.C.]: ERIC Clearinghouse microfiches : positive. 
Subjects Classification.  |  Document Delivery.  |  Electronic Libraries.  |  Information Systems.  |  Library Collections.  |  Periodicals.  |  Scholarly Journals.  |  Digital Technology Scanning  |  Bayes Theorem Digital Data Text Characteristics
Form/genre Reports, Research.  |  Speeches/Meeting Papers.
Other authors/contributors Soda, Giovanni, author  |  Vullo, Alessandro, author
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mc 2253 ED459814 Main Reading Room - Newspapers and Family History
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