In this paper, we present our empirical studies on learning a state-of-the-art English spelling error correction system using Oxford corpora: from detecting spelling errors, building candidate sets, to finally selecting the most likely candidate corrections. With a log-linear model framework, we integrated features based on spelling correction ngrams from supervised data, probabilistic edit-distance, and various distributional similarities. In particular, we did an empirical comparison over these measures, showing the effectiveness for especially the probabilistic edit-distances. We obtained significantly better F-measures, achieving a relative improvement of 49% over Microsoft Word.
By: Ming Sun, Bing Zhao
Published in: RC24912 in 2009
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