Integration of bottom-up and top-down contextual knowledge in text error correction

Authors:
Sargur N. Srihari;Jonathan J. Hull;Ramesh Choudhari
Affiliations:
State University of New York at Buffalo, Amherst, New York;State University of New York at Buffalo, Amherst, New York;State University of New York at Buffalo, Amherst, New York
Venue:
AFIPS '82 Proceedings of the June 7-10, 1982, national computer conference
Year:
1982

Citing 1
Cited 1

Computer programs for detecting and correcting spelling errors

Communications of the ACM

Integrating diverse knowledge sources in text recognition

ACM Transactions on Information Systems (TOIS)

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Abstract

This paper presents an efficient method for the integration of two forms of contextual knowledge into the correction of character substitution errors in words of text: bottom-up knowledge in the form of character transitional probabilities and top-down knowledge in the form of a dictionary. The method is a modification of the Viterbi algorithm---which maximizes string a posteriori probability by using character confusion and transitional probabilities---so that only legal strings are output. The algorithm achieves its efficiency by using a trie structure representation of a dictionary in the search process. An analysis of the computational complexity and the results of experimentation with the approach are presented.