A Method for Unsupervised Broad-Coverage Lexical Error Detection and Correction

Nai Lung Tsao, David Wible

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

We describe and motivate an unsupervised lexical error detection and correction algorithm and its application in a tool called Lexbar appearing as a query box on the Web browser toolbar or as a search engine interface. Lexbar accepts as user input candidate strings of English to be checked for acceptability and, where errors are detected, offers corrections. We introduce the notion of hybrid n-gram and extract these from BNC as the knowledgebase against which to compare user input. An extended notion of edit distance is used to identify most likely candidates for correcting detected errors. Results are illustrated with four types of errors.

Original languageEnglish
Title of host publicationProceedings of the 4th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2009
EditorsJill Burstein, Claudia Leacock, Joel Tetreault
PublisherAssociation for Computational Linguistics (ACL)
Pages51-54
Number of pages4
ISBN (Electronic)9781932432374
StatePublished - 2009
Event4th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2009 - Boulder, United States
Duration: 5 Jun 2009 → …

Publication series

NameProceedings of the 4th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2009

Conference

Conference4th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2009
Country/TerritoryUnited States
CityBoulder
Period5/06/09 → …

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