A technique for computer detection and correction of spelling errors
Communications of the ACM
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Multilingual text induced spelling correction
MLR '04 Proceedings of the Workshop on Multilingual Linguistic Ressources
Managing misspelled queries in IR applications
Information Processing and Management: an International Journal
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We present TISC, a language-independent and context-sensitive spelling checking and correction system designed to facilitate the automatic removal of non-word spelling errors in large corpora. Its lexicon is derived from a very large corpus of raw text, without supervision, and contains word unigrams and word bigrams. It is stored in a novel representation based on a purpose-built hashing function, which provides a fast and computationally tractable way of checking whether a particular word form likely constitutes a spelling error and of retrieving correction candidates. The system employs input context and lexicon evidence to automatically propose a limited number of ranked correction candidates when insufficient information for an unambiguous decision on a single correction is available. We describe the implemented prototype and evaluate it on English and Dutch text, containing real-world errors in more or less limited contexts. The results are compared with those of the isolated word spelling checking programs ISPELL and the MICROSOFT PROOFING TOOLS (MPT).