Combining Trigram-Based and Feature-Based Methods for Context-Sensitive Spelling Correction

Combining Trigram-based and Feature-based Methods for Context-Sensitive Spelling Correction Andrew R. Golding and Yves Schabes Mitsubishi Electric Research Laboratories 201 Broadway Cambridge, MA 02139 {golding, schabes}@merl, com Abstract This paper addresses the problem of cor- recting spelling errors that result in valid, though unintended words (such as peace and piece, or quiet and quite) and also the problem of correcting particular word usage errors (such as amount and num- ber, or among and between). Such cor- rections require contextual information and are not handled by conventional spelling programs such as Unix spell. First, we introduce a method called Trigrams that uses part-of-speech trigrams to encode the context. This method uses a small num- ber of parameters compared to previous methods based on word trigrams. How- ever, it is effectively unable to distinguish among words that have the same part of speech. For this case, an alternative feature-based method called Bayes per- forms better; but Bayes is less effective than Trigrams when the distinction among words depends on syntactic constraints. A hybrid method called Tribayes is then in- troduced that combines the best of the pre- vious two methods. The improvement in performance of Tribayes over its compo- nents is verified experimentally. Tribayes is also compared with the grammar checker in Microsoft Word, and is found to have sub- stantially higher performance. 1 Introduction Spelling correction has become a very common tech- nology and is often not perceived as a problem where progress can be made. However, conventional spelling checkers, such as Unix spell, are concerned only with spelling errors that result in words that cannot be found in a word list of a given language. One analysis has shown that up to 15% of spelling errors that result from elementary typographical er- rors (character insertion, deletion, or transposition) yield another valid word in the language (Peterson, 1986). These errors remain undetected by tradi- tional spelling checkers. In addition to typographical errors, words that can be easily confused with each other (for instance, the homophones peace and piece) also remain undetected. Recent studies of actual ob- served spelling errors have estimated that overall, errors resulting in valid words account for anywhere from 25% to over 50% of the errors, depending on the application (Kukich, 1992). We will use the term context-sensitive spelling cor- rection to refer to the task of fixing spelling errors that result in valid words, such as: (1) * Can I have a peace of cake? where peace was typed when piece was intended. The task will be cast as one of lexical disambigua- tion: we are given a predefined collection of confu- sion sets, such as {peace,piece}, {than, then}, etc., which circumscribe the space of spelling errors to look for. A confusion set means that each word in the set could mistakenly be typed when another word in the set was intended. The task is to predict, given an occurrence of a word in one of the confusion sets, which word in the set was actually intended. …

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