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An improved error model for noisy channel spelling correction

Authors:

Eric Brill,

Robert C. MooreAuthors Info & Claims

ACL '00: Proceedings of the 38th Annual Meeting on Association for Computational Linguistics

Pages 286 - 293

https://doi.org/10.3115/1075218.1075255

Published: 03 October 2000 Publication History

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Abstract

The noisy channel model has been applied to a wide range of problems, including spelling correction. These models consist of two components: a source model and a channel model. Very little research has gone into improving the channel model for spelling correction. This paper describes a new channel model for spelling correction, based on generic string to string edits. Using this model gives significant performance improvements compared to previously proposed models.

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Sharma MGogineni ARamakrishnan N(2024)Neural Methods for Data-to-text GenerationACM Transactions on Intelligent Systems and Technology10.1145/366063915:5(1-46)Online publication date: 8-May-2024
https://dl.acm.org/doi/10.1145/3660639
Ye DTian BFan JLiu JZhou TChen XLi MMa JFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Improving Query Correction Using Pre-train Language Model In Search EnginesProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614930(2999-3008)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614930
Pande MKakkar VBansal MKumar SSharma CMalhotra HMehta PAl Hasan MXiong L(2022)Learning-to-Spell: Weak Supervision based Query Correction in E-Commerce Search with Small Strong LabelsProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557113(3431-3440)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557113
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An improved error model for noisy channel spelling correction
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ACL '00: Proceedings of the 38th Annual Meeting on Association for Computational Linguistics

October 2000

598 pages

General Chair:
Hitoshi Iida

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Association for Computational Linguistics

United States

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Published: 03 October 2000

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Overall Acceptance Rate 85 of 443 submissions, 19%

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Sharma MGogineni ARamakrishnan N(2024)Neural Methods for Data-to-text GenerationACM Transactions on Intelligent Systems and Technology10.1145/366063915:5(1-46)Online publication date: 8-May-2024
https://dl.acm.org/doi/10.1145/3660639
Ye DTian BFan JLiu JZhou TChen XLi MMa JFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Improving Query Correction Using Pre-train Language Model In Search EnginesProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614930(2999-3008)Online publication date: 21-Oct-2023
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A spelling correction program based on a noisy channel model

A hybrid model for spelling error detection and correction for Urdu language

Statistical Context-Sensitive Spelling Correction Using Typing Error Rate

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