Toward filling the gap between interactive and fully-automatic spelling correction using the linguistic context

We report on the comparison of different strategies for correcting spelling errors resulting in non-existent words. Unlike interactive spelling checkers, where usually only the left context is available, the system we developed takes advantage of the entire context surrounding misspelling. Moreover, unlike traditional systems, based exclusively on a string-to-string edit distance and a word language model, we explore the use of the part-of-speech for selecting candidates. In conclusion, we show that spelling correction improves by extending the context. The best results are obtained when combining a part-of-speech filter with a word language model, and using both the left and right adjacent contexts.