Multi-Level Feature Extraction for Spelling Correction

For an advanced implementation of spelling correction via machine learning, a multi-level featurebased framework is developed. In order to use as much information as possible, we simultaneously include features from the character level, phonetic level, word level, syntax level, and semantic level. These are evaluated by a support vector machine to predict the correct candidate. Our method allows to correct non-word errors as well as real-word errors simultaneously using the same feature extraction methods, and it closes the gap separating isolated error correction techniques from context-sensitive methods. In contrast to previous approaches, our technique is not confined to correct only words from precompiled lists of “confused ” words. Regarding the correction capabilities of our system, we outperform Microsoft Word, Google, Hunspell, Aspell and FST in recall by at least 3 % even if confined to non-word errors. The recall of our system ranges from 90 % for the first candidate to 97 % for all five candidates presented. Index Terms — context-sensitive spelling correction, lexical disambiguation, machine learning, isolated error correction. I