We have designed, implemented and evaluated an end-to-end system spellchecking and autocorrection system that does not require any manually annotated training data. The World Wide Web is used as a large noisy corpus from which we infer knowledge about misspellings and word usage. This is used to build an error model and an n-gram language model. A small secondary set of news texts with artificially inserted misspellings are used to tune confidence classifiers. Because no manual annotation is required, our system can easily be instantiated for new languages. When evaluated on human typed data with real misspellings in English and German, our web-based systems outperform baselines which use candidate corrections based on hand-curated dictionaries. Our system achieves 3.8% total error rate in English. We show similar improvements in preliminary results on artificial data for Russian and Arabic.
More recently, REF incorporated data from the World Wide Web into an n-gram based model, significantly improving spell checking accuracy using unsupervised learning.
Using the Web for Language Independent Spellchecking and Autocorrection
EMNLP
Computer Science