On using context for automatic correction of non-word misspellings in student essays
Michael Flor, Yoko Futagi · North American Chapter of the Association for Computational Linguistics · 2012
In this paper we present a new spell-checking system that utilizes contextual information for automatic correction of non-word misspellings. The system is evaluated with a large corpus of essays written by native and non-native speakers of English to the writing prompts of high-stakes standardized tests (TOEFL® and GRE®). We also present comparative evaluations with Aspell and the speller from Microsoft Office 2007. Using context-informed re-ranking of candidate suggestions, our system exhibits superior error-correction results overall and also corrects errors generated by non-native English writers with almost same rate of success as it does for writers who are native English speakers.